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Drivers of decoupling economic growth from carbon emission – an empirical analysis of 192 countries using decoupling model and decomposition method
Abstract
All countries are committed to tackle global warming whatever the economic development level. However, governments are hesitant to take aggressive actions to cut carbon emissions without breakthroughs in decoupling economic growth from carbon emissions if they have to pay the economic loss price. Thus developing and implementing more aggressive and efficient Intended Nationally Determined Contributions (INDCs) of the Paris Agreement requires a better understanding of the possibility to decouple carbon emissions from economic growth. This study explores global and regional decoupling trends and further investigates the decoupling effects using the upgraded data. The results demonstrate that decoupling states of developed countries mostly converged on stable weak decoupling and switching to the strong decoupling status. Most developing countries did not show a clear decoupling state. Affluence level was the key offsetting effect of decoupling process, while energy intensity was the most significant effect to promote the decoupling process. Also, declines of energy intensity drove developed countries to strong decoupling state. Nonetheless, economic growth level dominated the decoupling process in developing countries, by contrast, there was no significant decreasing trend of energy intensity in these countries. These findings have feasible policy implications for implementing the INDCs.
... As the world's largest developing country, China's carbon emission reduction work plays a crucial role in improving the grim situation of global climate change. In the past decade, China has formulated a series of policy measures to try to realize the decoupling of economic growth and carbon emissions [2]. Although it has achieved some results, it still faces severe challenges. ...
... For example, Huang et al. [13] proposed a nonlinear multivariate gray prediction model based on EKC in view of the realistic characteristics of incomplete statistics of carbon emissions data of the transportation sector, considering factors such as economy, population and energy. (2) The relationship between transportation carbon emissions and economic growth was discussed with the help of decoupling models, including the Tapio decoupling model, Kaya identity and log-mean variance index (LMDI) decomposition model. Jiang et al. [14] combined the decomposition technology with decoupling analysis and decomposed the traffic decoupling index into five different aspects to analyze the key driving factors of the decoupling of CO 2 emissions related to different transportation modes and transportation turnover. ...
... Economic development is represented by the GDP of a city. At the same time, in order to provide the accuracy of the estimation results, four control variables are selected into model (1) and model (2) as follows: ...
The proposal of China’s “double carbon target” means that China is trying to realize the decoupling between economic development and carbon emissions. Based on the dual perspectives of velocity decoupling and quantitative decoupling, this paper systematically analyzes the decoupling state between transportation carbon emissions and economic development in 284 Chinese cities from 2006 to 2020 by using the Tapio decoupling model and the environmental Kuznets curve model. The results show the following: (1) From the perspective of velocity decoupling, most China’s cities have initially realized the decoupling state of transportation carbon emissions and urban economic development, entering the stage of weak decoupling, but not yet into the stage of strong decoupling, which indicates the decoupling level still needs to be improved. In space, the regions with high decoupling levels show the spatial differentiation characteristics of more in the east and middle, and less in the west. (2) From the perspective of quantitative decoupling, the relationship between urban transportation carbon emissions and economic development presents an inverted U-shaped EKC curve in China, and all cities have basically not crossed the inflection point and not entered the absolute decoupling state, but are in the trend of evolving to the quantitative decoupling state. This conclusion also verifies the view that velocity decoupling is generally in the weak decoupling stage. (3) The quantitative decoupling analysis also shows that urban population density, urban road density and per capita private car ownership all can worsen transportation carbon emissions, while public transport efficiency is the key driving forces for industrial carbon emission reduction. This study will help policymakers and practitioners to better understand the decoupling relationship between urban economic development and transportation carbon emissions in China, so as to formulate a strategy that fits China’s characteristics to achieve the “double carbon target” for transportation sector.
... Some academics have proposed the decoupling paradigm, which states the need to separate economic growth from increasing energy demand [55]. However, this approach has been shown to be more relevant and applicable to cases of developed economies, where energy efficiency and decarbonisation measures are much more advanced than in developing countries [56]. Research into energy planning and demand modelling for developing countries has continued to recognise the reliability of socio-economic ...
... Some academics have proposed the decoupling paradigm, which states the need to separate economic growth from increasing energy demand [55]. However, this approach has been shown to be more relevant and applicable to cases of developed economies, where energy efficiency and decarbonisation measures are much more advanced than in developing countries [56]. Research into energy planning and demand modelling for developing countries has continued to recognise the reliability of socio-economic indicators as an indicator of energy demand [14,56]. ...
... However, this approach has been shown to be more relevant and applicable to cases of developed economies, where energy efficiency and decarbonisation measures are much more advanced than in developing countries [56]. Research into energy planning and demand modelling for developing countries has continued to recognise the reliability of socio-economic indicators as an indicator of energy demand [14,56]. Consequently, this study assumes a relationship between energy demand and socio-economic development. ...
Sierra Leone is an electricity-poor country with one of the lowest electricity consumption per capita rates across sub-Saharan Africa. Yet, with ambitious targets to transform and stimulate its economy in the coming decades, energy demand forecasting becomes an integral component of successful energy planning. Through applying the MAED-D (version 2.0.0) demand software, this research study aims to generate Sierra Leone’s electricity demand forecasts from 2023 to 2050. Three novel scenarios (baseline-, high-, and low-demand) are developed based on socio-economic and technical parameters. The baseline scenario considers the current electricity sector as business-as-usual; the high-demand scenario examines an ambitious development future with increased economic diversification and mechanisation, and the low-demand scenario examines more reserved future development. The modelled scenario results project an increase in electricity demand ranging from 7.32 PJ and 12.23 PJ to 5.53 PJ for the baseline-, high-, and low-demand scenarios, respectively, by 2050. This paper provides a base set of best-available data needed to produce an electricity demand model for Sierra Leone which can be used as a capacity-building tool for in-country energy planning alongside further integration into data modelling pipelines.
... Comparative studies of decoupling trends reveal strong decoupling in developed countries, while developing nations experience weak and unstable decoupling. China's stabilization and optimization process outpaces that of Brazil and India [5]. Research across various regions underscores the vital role of decoupling theory in informing policy development, optimizing resource distribution, and advancing green growth. ...
... Existing research mainly uses two main approaches for decoupling analysis: the OECD decoupling index, which compares initial and final values, and the Tapio decoupling elasticity method, which focuses on state-level analysis. In contrast to the OECD method, which may involve subjectivity in selecting a base year, the Tapio method enables year-to-year comparisons, offering a more objective assessment of the decoupling between economic growth and carbon emissions [5]. Also, the Tapio method introduces the concepts of decoupling elasticity and mediating variables, enabling dynamic analysis of the sensitivity of influencing factors. ...
As global climate change intensifies, carbon emissions have become a paramount concern for policymakers and researchers. These emissions undermine ecosystem integrity and pose considerable obstacles to economic development and long-term sustainability. As the worlds largest carbon emitter, China faces substantial pressure to mitigate its emissions. Jiangxi Province, a critical region in the country, has experienced rapid economic growth, driving significant research into the relationship between energy consumption and carbon emissions. This paper examines data from 2010 to 2022, including Jiangxis GDP and total energy consumption, to estimate carbon emissions. In addition, through rigorous correlation and regression analyses, a significant positive association between carbon emissions and economic growth is identified, culminating in the development of a univariate regression model to quantitatively assess this relationship. Employing the Tapio decoupling elasticity method, the study evaluates the dynamic decoupling relationship between carbon emissions and economic growth, revealing a state of weak decoupling. The results indicate that despite a reduction in carbon intensity relative to economic growth, Jiangxis total carbon emissions continue to rise. To achieve absolute decoupling, the province will need to implement more effective and comprehensive policy measures.
... There is ample literature on decoupling growth from emissions CO2. This topic sparks growing interest from scientists because of the increasing global warming mostly caused by CO2 emissions which threaten the global climate and considerably affect both the environment and human health (Wang and Su, 2020;Li and Jiang 2017;Thalassinos et al., 2022). ...
... Other factors showed little impact on the decoupling of CO2 emissions. Wang et al. (2020) constructed a decoupling effort index for the Chinese iron and steel industry. ...
... " this study compares the decoupling progress in East China with that of developed countries and offers policy recommendations for replication in less economically developed regions. The United States exhibited a relatively stable decoupling status between 2000 and 2014, followed by significant decoupling 50,51 . In 2006, China overtook the US to take the top spot for carbon dioxide emissions worldwide. ...
Urban energy consumption is mostly concentrated in industrial regions, and carbon emissions from industrial land use have significantly increased as a result of fast urbanization and industrialization. In the battle against climate change, the affluent regions of developing countries are increasingly being used as models for reducing carbon emissions. Therefore, in order to accomplish global sustainable development, it is crucial to understand how industrial land use and carbon emissions are decoupled in wealthy areas of rising nations. This study investigates the decoupling effects and the factors influencing them in six East Chinese provinces and one city between 2005 and 2020 using the Tapio decoupling model and the LMDI decomposition approach. At the same time, the industrial carbon emissions from 2021 to 2035 were predicted using a BP neural network model combined with scenario analysis. The findings indicate that: (1) From 29.921 million tons in 2005 to 40.2843 million tons in 2020, the carbon emissions from industrial land in the East China area have nearly doubled. Of these, Shandong and Jiangsu emit more than half of the region’s total emissions around East China. (2) The decoupling effect analysis shows the East China region’s decoupling trajectory’s phased characteristics, with the degree of decoupling gradually increasing from weak decoupling (2006–2012) to strong decoupling (2013–2018) and finally to negative decoupling (2019–2020). (3) The primary causes of the rise in carbon emissions in the East China region are the scale of per capita economic output and industrial land use. (4) The overall industrial carbon peak time in East China is roughly distributed between 2028 and 2032. It is expected that Shanghai, Shandong, Jiangsu, and Zhejiang will be among the first to achieve carbon emission peak.
... Considering the sharp contradiction between economic growth and environmental protection that would hinder sustainable development, green growth strategies aiming to make the economy green have certainly been at the center of policy-making at the United Nations, G20, European Union, and many other countries 48 . The level of decoupling is varied across countries with different level of wealth, while a few factors such as energy intensity and the level of financial development are commonly found as drivers for decoupling energy intensity and economic development 49,50 . Future studies that identify factors that foster decoupling impact and reveal causal linkages among them are needed to assess the validity of the current policy framework for green growth, which could serve as a basis for future climate policy. ...
Countries have pledged commitment to the 2030 Sustainable Development Goal (SDGs) and the Paris Agreement to combat climate change. To maximize synergies between SDGs and climate actions (CAs), we evaluate the alignment of national commitment to SDGs and emissions reduction targets by comparing action plans embodied in Voluntary National Review (VNR) reports and the Nationally Determined Contributions (NDCs) across 67 countries. An Artificial Intelligence (AI)-based approach is proposed in this study to explore the interconnectedness by applying machine learning classifier and natural language processing. Middle- and low-income countries with high emissions tend to have low NDC targets and contain similar information in VNR reports. High-income countries show less alignment between their NDCs and VNRs. The economic status of countries is found to be connected to their climate actions and SDGs alignment. Here, we demonstrate utility and promise in using AI techniques to unravel interactions between CA and SDG.
... Between 2012 and 2021, the global annual average of carbon emissions soared to 36 billion tons (Forster et al., 2022). Excessive carbon emissions have severely damaged the global ecological environment, leading to an increasing focus on carbon reduction by more and more countries (Wang & Su, 2020;Daneshmand-Mehr & Ghane, 2021). In 2006, China's carbon emissions surpassed those of the United States, reaching 6.2 billion tons (International Energy Agency (IEA), 2008). ...
Amid the increasing global demand for carbon reduction, the Fintech Ecosystem (FES) has emerged as a crucial avenue for advancing green economic transformation and reducing urban carbon emissions (UCE), while its actual impact has yet to be thoroughly examined. Therefore, this study investigates how FES development influences UCE, both directly and indirectly, across 282 Chinese cities. This paper develops a comprehensive index system to evaluate FES and employs both fixed-effects panel models and spatial econometric models to examine the impact of FES on UCE across 282 Chinese cities from 2005 to 2020. The results indicate that FES plays a substantial role in decreasing UCE, a finding that withstands various robustness and the endogeneity tests. Mechanism tests reveal that FES can reduce UCE by enhancing green technology innovation, green finance and human capital. Heterogeneity analyses show that FES significantly mitigates UCE across both eastern and western regions and in non-resource-based cities. Furthermore, spatial analysis reveals substantial spillover effects, indicating that FES development in one city positively influences UCE in neighboring regions. The findings highlight the importance for governments to focus on fostering FES development and leveraging its potential to reduce UCE. The outcomes of this research are pivotal for the further cultivation of FES development and the crafting of viable strategies for UCE mitigation.
... Using structural decomposition analysis, Hubacek et al. (2021) find the existence of mini cycles where some countries experience decoupling followed by periods of intensification. Wang and Su (2020) observe that many developed nations experienced decoupling during the 2000-2014 period, but many developing nations did not experience decoupling. ...
This chapter provides an overview of advances in social science research on the anthropogenic drivers of climate change. Anthropogenic drivers refer to the social systems and human actions causing GHG emissions and societal factors shaping and conditioning those actions. The authors summarize the contemporary drivers research literature on (1) economic growth, (2) income and wealth inequality, (3) environmental load displacement and ecologically unequal exchange, (4) militarization, and (5) corporations. In addition to summarizing recent work, the authors identify future directions for each of these areas of research, as well as ways in which they can be better integrated.
... This cycle is further exacerbated by changes in industrial structure, urbanization, and economic growth, which also contribute to increased GHG emissions. Technological progress and environmental constraints both present new possibilities and requirements for energy intensity [35]. Based on the various effects summarized from existing research, Figure 1 illustrates the underlying mechanisms of the relationships among these driving factors and their connections to GHG emissions. ...
Southeast Asia is suffering from extreme weather, and its carbon emissions are increasing rapidly. For efficient emission reduction, clarifying the complex sources behind is necessary. This study takes a new perspective of incorporating multiple GHGs (greenhouse gases) into the same analysis framework, employing the STIRPAT model to dissect the contributions of various socio-economic factors to the emissions of CO2, CH4, and F-gases based on panel data. The analysis reveals that the driving coefficients of total population and urbanization rate are several to 10 times higher than those of other factors and can reach up to 2.98 and 4.715 and are the most significant drivers of GHG emissions in the region. Quadratic per capita GDP shows a significant positive driving effect, indicating that most Southeast Asian countries are unlikely to reach the Kuznets point in current development trajectories. The driving coefficients of F-gases in industrialized countries are significantly higher than those of other GHGs, indicating that their growth rate of F-gases will outpace that of CO2 and CH4. In countries with a similar industrial structure, the driving coefficient of CO2 from the secondary industry is up to 0.183 and down to 0.057, shows the influence of specific sector composition in the secondary industry on emissions. These findings provide critical insights for Southeast Asian policymakers aiming to develop effective climate policies.
... The aim is to highlight flexible and economically viable solutions that can be applied across sectors to effectively reduce carbon emissions Green technologies represent a transformative approach to industrial development in [5]. From renewable energy systems to carbon capture storage (CCS) processes, these technologies offer the potential to significantly reduce emissions while increasing operational efficiency and as businesses adapt to in this new paradigm not only can regulatory requirements be met but also competitive advantage can be achieved [6].This paper shows that green technology Provides a practical path towards sustainable industrial practices on a regular basis, providing scalable, efficient and economical solutions to one of the most pressing challenges of our time [7]. Figure 1 illustrates how the various environmental factors carbon footprint, energy footprint, water footprint, and nitrogen footprint are interconnected which, when combined, contribute to the overall environmental impact of the industrial sector [8]. ...
Due to energy-intensive processes, dependence on fossil fuels and poor waste management, industry is among the largest contributors to global carbon emissions This environment this growing environmental problem highlights the urgent need for sustainable solutions to reduce carbon emissions. This study examines the role of green technologies in addressing these challenges, focusing on renewable energy systems, carbon capture and storage (CCS), circular economic practices, and digital products on the other hand. The main objective is to assess how these technologies can reduce greenhouse gas emissions and increase energy efficiency and potentially increase the industrial sustainability of resources activities in the field. Findings show that integrating green technologies can lead to a 25% reduction in energy consumption, 85% efficiency in carbon capture, and greenhouse gas emissions has decreased by 50% in a decade Furthermore, sustainable materials and energy efficiency solutions promise to reduce waste and improve the relative quality of infrastructure use Display results. Despite constraints such as high start-up costs and technical barriers, the study highlights the significant potential of green innovation to transform technology It provides a call to action for businesses , policy makers and consumers to embrace this technology as a means to achieve global sustainability goals ends.
... These types of cities often rely on mineral resource extraction and processing industries with high carbon emissions, so their economic models are significantly characterized by extensive development paths of high input, high energy consumption, high emissions, and low efficiency, which have gradually become an important factor restricting China from achieving comprehensive carbon peak [3,4]. Therefore, the in-depth exploration of how MR-BCs can achieve decoupling between carbon dioxide emissions and economic growth has become a focus of attention for both academia and policymakers [5,6]. By using the EWM-TOPSIS model (EWM: Entropy weight method; TOPSIS: Technique for Order Preference by Similarity to Ideal Solution) to quantify the economic indices of 18 MRBCs in southwest Sustainability 2024, 16, 10081 3 of 23 but without clear representativeness. ...
Mineral resource-based cities (MRBCs) refer to cities with mining and processing of mineral resources as the main industry, so there is a close relationship between their economic development and resource consumption. However, this relationship often hinders its rapid transition towards economic diversification and low-carbon models. Based on quantifying the economic index level of 18 MRBCs in southwest China, this paper has employed the Tapio elasticity coefficient method (Tapio model) and Environmental Kuznets Curve (EKC curve) to analyze the decoupling effect between the economic index and carbon dioxide. After the deep research of the “decoupling” phenomenon and its dynamic changes between economic development and carbon emissions, this paper has aimed to explore a low-carbon transformation path suitable for each city. The research results have indicated that: (1) The overall trend of carbon dioxide emissions is increasing, but the growth rate is gradually slowing down, effectively controlling the situation of carbon dioxide emissions. (2) The economic index level shows an upward trend, and the growth rate gradually increases, which signifies a positive trend in economic development. (3) The decoupling effect began in MRBCs in southwest China in 2013, and the decoupling effect was achieved in 2019.
... In addition, many scholars explored the influencing factors of carbon emission decoupling by using the log-mean score difference index method and the production-theoretical decomposition analysis method. It is proposed that energy intensity, affluence, and population size are among the influencing factors of carbon emission decoupling [28,[52][53][54]. Some scholars have also used the geographically weighted regression model to discuss the effects of resident income, population size, and secondary industry scale on the coupling coordination degree between carbon emission and economic development [55]. ...
Coordinating the relationship between “dual carbon” targets and stable economic growth is crucial for promoting high-quality development in China. This study utilizes the coupling coordination model, kernel density estimation, and spatial econometric models to explore the temporal and spatial evolution characteristics and influencing factors of the coupling coordination degree between the promotion of the “dual carbon” targets and stable economic growth in 287 Chinese cities from 2011 to 2021. The results indicate that, in terms of temporal evolution, the promotion of China’s “dual carbon” targets increases yearly, while stable economic growth follows a “year-on-year increase—short-term decline—sustained recovery” pattern with the coupling coordination degree fluctuating upward. Regarding spatial evolution, the coupling coordination degree between the promotion of the “dual carbon” targets and stable economic growth in China presents a “higher in the east, lower in the west” spatial pattern, with varying gradient effects and polarization across the country and its regions. Influencing factors include government intervention, environmental regulations, energy efficiency, financial development, and R&D investment intensity. These findings provide scientific insights for addressing the mutual constraints between “dual carbon” targets and stable economic growth.
... Scholars like Qi [5] and Xin et al. [6] utilized the EIO-LCA model to investigate CO 2 emission influencing factors, highlighting the role of import/export and inflow/outflow structures in emission reduction. The LMDI (Logarithmic Mean Divisia Index) decomposition model, Tapio decomposition model, and distorted Kaya identity are commonly employed to analyze driving factors affecting carbon emissions, suggesting that energy consumption intensity and structure inhibit carbon emissions, while energy and industrial structure and economic growth promote them [7][8][9][10]. Tong et al. [11] established a VAR model to study the driving role of influencing factors on carbon emissions in different industrialization stages across 34 countries, offering insights for China's current low-carbon development phase. ...
The present study proposed a method to examine the carbon emissions of various departments in Zhejiang Province from 2003 to 2020 using the IPCC sectoral method. The use of the LMDI model analyzed the factors that influence carbon emission change in Zhejiang Province. The ARIMA prediction model and grey prediction model are utilized to forecast carbon emissions of Zhejiang Province in the future. The proposed measures for carbon emission reduction in Zhejiang Province are given, and some reference basis is provided for similar provinces to carry out low-carbon transformation. The results demonstrated that: (1) The carbon emission of Zhejiang Province from 2003 to 2020 shows a linear increase trend, with a growth rate of 172% during the 18 years. (2) The energy structure of Zhejiang Province is developing towards energy cleanliness. (3) Energy intensity and industrial structure are inhibiting effects, economic output and population size are promoting effects, and energy structure has both inhibiting and promoting times. (4) ARIMA’s prediction of carbon emissions in Zhejiang Province in the next few years is more accurate than that of the grey prediction model. The prediction results of ARIMA show that Zhejiang Province will usher in the carbon peak in 2025, while the grey prediction results show that it will not usher in the carbon peak before 2027.
... Common methods for estimating carbon emissions impact include the SBM model [33][34][35], regression models [36,37], and the Tapio decoupling model [38,39]. Regression models require high-quality data, while the SBM model focuses on resource allocation at a point in time, making it less effective for capturing the evolving relationship over time. ...
In the context of global and national carbon reduction targets, agricultural carbon emissions have become a critical focus. As global food demand increases, numerous agricultural policies have been implemented. Faced with limited policy resources, evaluating the impact of these policies on agricultural carbon emissions and production is essential. This study examined the relationship between food production and agricultural carbon emissions during the stage of agricultural development in Shandong Province, one of China’s major grain-producing regions, using the decoupling model. Additionally, the coupled coordination model was employed to assess the specific influence of agricultural policy clusters on this transformation. The results indicate that Shandong is transitioning from high-input, extensive farming to green, low-carbon, modern agriculture, with most cities shifting from strong negative decoupling to strong decoupling. Over time, the role of agricultural policies in driving this shift has grown more significant. Future policymaking should prioritize the overall quality of agricultural producers and maintain a continuous focus on sustainable, green development. Ensuring that policy directions align with evolving stages of agricultural development and adjusting them in real-time will be crucial.
... In recent years, the relationship between green technology innovation and carbon emission performance has become a global focus of attention [8][9][10]. Particularly against the backdrop of rapid economic development, how to achieve the coordinated development of economic growth and carbon emissions has become a key concern for policymakers and scholars around the world [11,12]. Green technology innovation refers to the process of reducing environmental burden and improving resource utilization efficiency through technological improvements and managerial innovations. ...
Amid the increasingly severe global climate change situation, green technology innovation has become an important means to promote carbon reduction and achieve the transition to a low-carbon economy. This study aims to systematically analyze the relationship between green technology innovation and carbon emission performance in the urban agglomeration of the middle reaches of the Yangtze River, exploring the degree of coupling and coordination between different cities. Utilizing data from 2011 to 2021, we employ methods such as the Coupling Coordination Degree Model and fixed effects model to achieve our objectives. Our findings reveal that both green technology innovation and carbon emission performance in this region are on an upward trend; however, the growth rate of green technology innovation showed a slowdown in 2021. Notably, there are disparities in the coupling coordination degree among cities, with economically developed areas exhibiting a faster growth rate. Moreover, green technology innovation significantly enhances carbon emission performance, and heterogeneity tests indicate that this impact is even more pronounced in cities with weaker environmental regulations. Despite regional differences, the overall trend remains positive.
... Wang and Su investigated the relationship between economic growth and carbon emissions in 192 countries and found that there was a clear decoupling state in developing countries, but the decoupling state in developed countries was mainly concentrated in a stable weak decoupling state that transitioned to a strong decoupling state. Among these, energy intensity had the most evidently positive effect on the decoupling process [18]. In their analysis of the relationships between the energy economies of major global regions and nations, Guo et al. also looked into the possibility of decoupling and discovered that, while energy use and economic growth have been rising simultaneously in developing economies, they show a typical inverted U-shaped decoupling relationship in industrialized nations [19]. ...
Clarifying the relationship between economic development and human well-being is conducive to promoting high-quality economic development. This study focused on 16 prefecture-level cities in the Qinghai-Xizang Plateau region. The critic weighting method assessed the 2007–2018 human well-being index (HWI). The Tapio decoupling model allowed the study of the human well-being decoupling state. Finally, the drivers of decoupling between economic development and human well-being were analyzed using the Logarithmic Mean Divisia Index method. The results indicated that (1) almost all cities in the study region had an upward 2007–2018 HWI trend, but there were significant differences in growth magnitude and change trend. (2) Economic development and human well-being in the study region in 2007–2018 had expansion negative decoupling, thus, human well-being increased with economic growth, but not as fast as gross domestic product. 9 cities in 2007 showed weak decoupling, expansion connection, and expansion negative decoupling, increasing to 13 cities by 2018, indicating that human well-being development gradually improved from 2007 to 2018. (3) For most cities, the economic scale effect was the most influential factor in the decoupling of economic development and human well-being. Therefore, this study provided policy recommendations for decoupling economic development and human well-being.
... [2,3] As a result of these restrictions, human activity has been severely limited, which has had a negative effect on economy and businesses on a global and national scale. [4][5][6] Apparently, the viable and impactful option for reducing COVID-19 from having a tragic effect on the globe was through a global lockdown. Wuhan City in China was the place of the first diagnosis of this deadly infectious disease on the 29 th of December 2019. ...
Humans’ lifestyles were radically changed after the 2020 COVID-19 epidemic. To mitigate the effects of the disease, most countries have implemented lockout systems. This research aims to provide some recommendations for long-term, sustainable environmental management by analyzing how the COVID-19 lockdown affected nitrogen dioxide (NO2), Ozone (O3), the land surface temperature (LST), and the normalized difference vegetation index (NDVI). Sentinel Hub data and LANDSAT 8 satellite imagery from 2017 to 2022 were used to create land use land cover (LULC), NDVI, and LST maps. Digital thematic maps of NO2 and O3 with figures that demonstrate the correlation between NDVI-LST and LULC-LST were derived using Arc geographic information system 10.8. The spatial analysis showed that LST and LULC are noticeably correlated, in addition to an inverse proportion between NDVI and LST, as well as between NO2 and O3. The lockdown in Erbil drastically decreased NO2 emissions by 54.5% and improved O3 levels by 7.6%. While the NDVI and O3 levels have increased significantly, the LST and NO2 levels have declined considerably due to the shutdown of all industrial and transportation activities during the lockdown. Although COVID-19 caused a lot of damage, the lockdown contributed to healthier conditions of the environment.
... Using this method, researchers have turned the factors that affect changes in carbon emissions into a number of different markers that can be used for different types of research. They have also conducted studies on a world scale [24], regional [25], national [26], city [27] and industrial [28] scales. ...
The Hu-Bao-O-Yu urban area is a major source of carbon emissions in China. It is also a major source of energy exports and high-end chemicals in China. Reaching peak carbon emissions early is especially important for meeting the national peak goal. For urban areas that rely on natural resources, we need to make it clearer how carbon emissions and economic growth affect each other and slowly break the strong link between the two. Therefore, in this paper, based on the data on carbon emissions, the decoupling state and the driving mechanism of carbon emissions in the Hu-Bao-O-Yu City group are researched by using the Tapio decoupling model and GDIM method. A new decoupling index model is constructed by combining GDIM and the traditional decoupling model. The main findings are as follows: (1) The Hu-Bao-O-Yu urban agglomeration, Ordos City, Baotou City and Yulin City have significant growth trends in annual carbon emissions, with Yulin City being the most important carbon source for the Hu-Bao-O-Yu urban agglomeration and its economic contribution to carbon emissions of the whole urban agglomeration is the most efficient. (2) The decoupling of Hu-Bao-O-Yu, Huhhot City, Baotou City, and Ordos City is dominated by expansionary negative decoupling, whereas Yulin City has strong negative decoupling. (3) The Hu-Bao-O-Yu urban cluster mainly affects the carbon intensity of fixed asset investments and output carbon intensity, which is a key part of the carbon separation process. The energy scale and structure also play a part in this process over time. (4) Changes in GDP per capita are a bigger part of changes in carbon emissions in the Hu-Bao-O-Yu urban agglomeration. These changes also determine the direction for changes in carbon emissions in the Hu-Bao-O-Yu urban agglomeration. In the future, the Hu-Bao-O-Yu urban agglomeration needs to coordinate its economic growth. Ordos and Yulin need to speed up the optimisation and transformation of their energy structures. Baotou needs to push for the low-carbon transformation of its industries. Huhhot needs to do more research on carbon sequestration technology and spend more on environmental protection. This will make the Hu-Bao-O-Yu urban agglomeration a resource-saving urban agglomeration and improve its ability to reduce emissions.
... Currently, clean and renewable energy is getting a lot of consideration. It is considered one of the most effective ways to decrease CE and reach a low-carbon development [218,219]. Le Qu et al. [220] recently asserted that substituting renewable energy for fossil fuel consumption would reduce carbon emissions. Several studies [221,222] have deliberated on openings and challenges associated with the transition toward a greener future. ...
Climate change has become a global nightmare, and the awareness of the causes of carbon emissions have resulted in rigorous studies. These studies linked the increase in global warming with booming economic growth. Since global warming has become more apparent, researchers have explored ways to decouple economic activities from carbon growth. Economic and carbon growth must be decoupled to achieve a low-carbon economy to support the carbon growth plan or emission reduction strategy. The world is transitioning towards a carbon-neutral and green ecosystem, so finding ways to decouple carbon emissions from economic activities is an exciting topic to explore. The study reviews current information on the importance of decoupling energy from economic growth innovative techniques that thoroughly examine the challenges and constraints of low-carbon energy systems. This review revealed that decarbonization and dematerialization had been achieved without declining global economic growth. It also provides information on energy use and economic activities leading to global carbon emissions and alternative solutions to the global challenge of climate change. The decoupling methods commonly used to determine the impact of energy decarbonization on economic growth are explored. All suggested that economic growth is a primary mover of global carbon emission increase and must be separated to achieve a carbon environment.
... Peluang yang diciptakan oleh komposisi penduduk ini tentu harus dimanfaatkan dengan optimal karena tidak semua negara berhasil memanfaatkan momentum ini. Brazil dan Afrika Selatan adalah dua diantara negara berkembang yang gagal menjadikan peluang ini sebagai pendorong percepatan pertumbuhan ekonomi dan peningkatan kesejahteraan bagi negaranya (Wang & Su, 2020). Oleh karena itu, diperlukan strategi untuk dapat menjadikan komposisi penduduk ini sebagai titik tolak kemajuan bangsa. ...
Unemployment among educated people makes a significant contribution. One of the triggers is the low interest in entrepreneurship among students. This is indicated by students' low participation in activities related to entrepreneurship since they were in college. After graduation, students prefer careers as employees or civil servants rather than entrepreneurs. This study aims to determine the extent to which self-efficacy, family support, and entrepreneurship education affect the interest in entrepreneurship either simultaneously or partially. This study uses a quantitative survey method with an explanatory survey design. The research population is students of the Department of Economics Education, Siliwangi University, Class of 2020. The sampling technique is saturated sampling, which totals 112 students. The data collection technique uses a questionnaire, and the data analysis technique uses the SPSS 25.0 for Windows program. The results showed: 1) There is a significant influence between self-efficacy on the interest in entrepreneurship with a significance value of 0.030; 2) There is no significant effect between family support on the interest in entrepreneurship with a significance value of 0.405; 3) There is no significant effect between entrepreneurship education on interest in entrepreneurship with a significance value of 0.321 4) There is a significant influence between self-efficacy, family support and entrepreneurship education on interest in entrepreneurship with a significance value of 0.000.
... Regions that were more proactive in adopting new technologies and enforcing strict environmental regulations showed more favorable decoupling states by the end of the study period. This is consistent with the previous work which found that technological innovation and stringent environmental policies are pivotal in decoupling economic growth from environmental degradation [54]. As Xinjiang continues to develop, maintaining a focus on regional specificities will be essential for crafting policies that not only promote economic growth but also safeguard environmental integrity. ...
Understanding the spatiotemporal decoupling effects among economic development, energy consumption, and carbon dioxide emissions is paramount to achieving sustainable development. This relationship sheds light on how regions can grow economically while managing their energy resources efficiently and minimizing environmental impacts. This study examines the critical and globally relevant issue of spatiotemporal decoupling that includes economic development, energy consumption, and carbon dioxide emissions in Xinjiang Province from 2006 to 2020. The Tapio Elasticity Analysis Method is utilized to achieve this objective. We found that the early years showed expansive coupling, reflecting a phase where economic growth was closely tied to increases in energy consumption and emissions. However, over time, particularly post-2010, there is a noticeable shift towards weak decoupling and eventually to more substantial forms of decoupling. The primary sector displayed mostly weak and strong decoupling. The secondary sector, however, showed fluctuating decoupling states. In the tertiary sector, a generally weak decoupling was observed. A spatial analysis across Xinjiang’s prefectures and cities revealed pronounced regional variations. This investigation validates the effectiveness of regional ecological policies and illustrates the necessity of tailored strategies to foster sustainable development. Our findings provide valuable insights not only for regional policymakers but also for international stakeholders aiming to achieve sustainable development. The results underline the importance of tailored strategies in different regions, contributing to the broader understanding of sustainable development dynamics.
... Decoupling analysis between economic growth and carbon emissions has been extensively conducted by scholars at various levels. Global-level studies by Shuai et al. (2019) and Wang and Su (2020) are complemented by regional examinations, such as those on ASEAN countries , OECD countries (Chen et al., 2018), and the European Union (Madaleno & Moutinho, 2018). At the national level, studies on specific countries like Colombia (Román-Collado & Cansino, 2018), Australia (Leal et al., 2019), Turkey (Ozdemir, 2023), and China (Pan et al., 2022;Wang et al., 2018a) provide valuable insights. ...
China faces the formidable challenge of balancing economic growth with carbon emissions reduction. Therefore, clarifying the decoupling relationship between the two is crucial for formulating effective carbon mitigation policies and achieving this balance. Previous studies often investigate the decoupling at a static perspective that may introduce some errors. In this paper, we propose to conduct such research from a dynamic perspective, and we apply it to carry out a study focusing on China’s provincial industrial carbon emissions (ICE) and industrial value-add (IVA) from 2001 to 2020. Additionally, using the Log-Mean Divisia Index (LMDI), we further explored the factors influencing changes in ICE. The results underscore the necessity of a dynamic perspective due to the observed non-linear interannual variation observed in ICE, IVA, industrial energy consumption (IEC), and population. Most provinces exhibit an increasing decoupling trend between ICE and IVA, with the majority still in a status of weak decoupling over the long-term interval (2001–2020). Among the four factors analyzed, energy intensity and economic intensity emerge as the primary contributor to decreasing and increasing ICE, respectively. Carbon intensity and population exhibit both positive and negative effects on increasing ICE, with the positive generally outweighing the negative. To effectively reduce carbon emissions, China should tailor differentiated policies for each provincial region considering both their absolute emission amounts and decoupling evolution trajectories. Furthermore, efforts should be made to expand the use of renewable energy, optimize the energy mix, and adopt cleaner technologies.
... In addition, the Chinese government also emphasizes that the regions with more severe carbon peaking situations should also strive to be peaking carbon emissions at the same time as the whole country. In fact, for the same scale of embodied carbon outflows, the stress on regions with severe peaking situations will be much greater than that on the regions with moderate peaking situations [19,20]. Therefore, in the context of all regions seeking their own carbon peak, different peaking situations will inevitably lead to different peaking stress, which requires a differentiated focus on the embodied carbon flows caused by domestic trade and a targeted mitigation strategy. ...
Embodied carbon flows among regions have led to unfair carbon emission responsibility accounting based on production. However, the heterogeneity of carbon peaking stress between regions is significantly neglected for those embodied carbon flows. Incorporating the carbon peaking stress into the embodied carbon flows can more clearly show what causes the carbon peaking stress and which carbon flow paths are more critical. In this study, the decoupling index of carbon emissions and economy development was applied to characterize the carbon peaking stress in each region, and the environmental extended multi-regional input–output model was applied to re-evaluate the criticality of regional embodied carbon flows. The results showed that the carbon peaking stress in China improved from 2007 to 2012, but the rebound of carbon peaking stress in 2017 made most regions reverse the previous downward trend. The stress to reach carbon peaks varies considerably from region to region, and the stress in the northwest is much higher than that in developed eastern China. Considering the heterogeneity of carbon peaking stress, additional concerns should be given to the net embodied carbon output in the northwestern, northern, and central regions, which can help avoid the dilemma between outsourcing embodied carbon and reducing carbon emissions from production. The policy to reduce emissions should be implemented in all regions that benefit from the net embodied carbon output of the northern and northwestern regions, where the carbon peaking stress is higher. The focus should be on the actual improvement of the carbon peaking stress, not just on the transfer of stress. The increasing urgency of achieving carbon peaking targets and unequal stress for regional peaking emissions calls for differentiated regional mitigation measures to help the Chinese government scientifically and in an orderly manner promote the overall and local carbon peaking work.
... In terms of the subject, some scholars have highlighted this topic with a certain country [13][14][15][16], or a certain region [17][18][19]. From the perspective of methodology, Tapio decoupled elasticity coefficient theory quantitatively analyses the situation where the pollution emissions growth rate changes as the economic growth rate changes, utilizing the decoupling factors proposed by the Organization for Economic Co-operation and Development (OECD) [20,21]. Meanwhile, index decomposition analysis (IDA), structural decomposition analysis (SDA), and production-theory decomposition analysis (PDA) are the main decomposition techniques for carbon emission components. ...
The unavoidable option for socially sustainable development is a low-carbon economy. One of the essential steps for China to attain high-quality development is reducing carbon emissions. It is necessary to realize low-carbon development in Sichuan, as it is not only an important economic zone but also an ecological protected area. The concurrent relationship among energy consumption, carbon emissions, and economic growth was examined in this study using the Tapio decoupling indicator, and the factors affecting energy consumption and carbon emissions in Sichuan were broken down using the logarithmic mean Divisia indicator (LMDI). The findings demonstrate a fundamental relative decoupling relationship between Sichuan’s energy use and carbon emissions. Analysis of energy consumption and carbon emissions in Sichuan Province from 2005 to 2020 shows distinct patterns. From 2005 to 2012, in 2014, and from 2016 to 2020, the relationship between energy use and carbon emissions was relatively decoupled, with decoupling values ranging between 0 and 1. Absolute decoupling occurred in specific years: 2010, from 2013 to 2018, and in 2020. These periods are characterized by economic growth alongside reductions in carbon emissions. Factors affecting energy consumption and carbon emissions were consistently analyzed, showing similar impacts throughout the study periods. We find that population and economic growth are the main driving forces of these effects. The effects of energy intensity and industrial structure mainly play restraining roles, and the latter has a slightly weaker effect than the former.
... This sentiment is reinforced by long-standing theories of economic and environmental "de-coupling," where affluent economies supposedly move towards cleaner forms of development decoupled from environmentally intensive industrialization (Mol and Spaargaren 2000;Mol 2002;Buttel 2000). This logic is still prominent today, especially in relation to carbon emissions, the impact of growth in rich nations, or the continued allure of green capitalism (Rathi 2024;Wang and Su 2020;Krugman 2023). ...
Capitalism is often described as a “growth machine,” whose insatiable growth imperative(s) must either be halted to protect ecologies or repurposed by some kind of socialist or social democratic state. However, the continuation of robust growth rates in many countries seems unlikely under present conditions. As monopoly capital scholars have long theorized, late-stage capitalist economies are characterized by a “cancerous malaise” of secular stagnation. In accordance with this perspective, financialization emerged to overcome challenges associated with this cancerous malaise. Using the U.S. economy as a case study, low growth and accelerating accumulation are interlinked under the socio-historic conditions of monopoly finance capital. This interrelation is captured by the term “stagnation-accumulation treadmill,” which stresses that the expansion of finance ensures the systemic requirement to increase material throughputs despite rising obstacles to robust growth. I argue that this interrelationship can help explain the paradox of increased consumption in the Global North alongside increased economic immiseration of the non-propertied working class. This argument stresses the continued materiality of a growth-dependent capitalist economy, even—if not especially—one characterized by stagnation and the explosion of so-called “non-real” finance sectors.
Abstract
Given the global focus on climate change and sustainable development, understanding the impact of China's fiscal decentralization on carbon emissions is essential for supporting green transformation and achieving carbon peaking and neutrality targets. This study examines data from 288 Chinese cities between 2003 and 2021, using fixed effects, moderation effects, and threshold effects models to assess the relationship between fiscal decentralization and carbon emissions, alongside its interaction with environmental regulation, economic development, industrialization, and city hierarchy. The results show that fiscal decentralization significantly aids carbon reduction, but stronger environmental regulations have not effectively slowed emission growth, creating a "green paradox." Relaxed regulations prioritize short-term economic gains, leading to underinvestment and fragmented regulatory efforts, which weaken emissions control. As economic development advances, fiscal decentralization's impact on carbon emissions shifts from positive to negative. Additionally, with increasing industrialization, the negative influence of fiscal decentralization on emission intensity becomes more pronounced. In non-first-tier cities, weak regulatory enforcement and limited autonomy hinder effective carbon reduction policies. To address these issues, the central government should enhance guidance and oversight of local environmental policies, ensure strict enforcement, optimize fiscal resources for environmental investment, and promote green economic transformation. A tailored approach to fiscal decentralization is needed to balance economic growth with environmental protection, achieving mutually beneficial outcomes.
Historically, economic growth has been closely coupled to carbon emissions responsible for climate change, but to stabilize global mean temperature, net-zero carbon emissions are necessary. Some economies have begun to reduce emissions while continuing to grow, but this decoupling is not fast enough to achieve global climate targets. Subnational climate actions seem to be crucial for the achievement of these targets. Here, we uncover the effectiveness of subnational efforts by estimating decoupling rates and CO2 emission intensities over the last three decades for over 1,500 subnational regions, encompassing 85% of global emissions, using global data on reported economic output and gridded production-based emissions. Thirty percent of regions with available data have fully decoupled, with higher-income and historically carbon-intensive regions exhibiting higher rates of decoupling and declining emission intensity. Countries of the Organization for Economic Co-operation and Development with greater spending on subnational climate actions show higher decoupling rates, as do subnational regions in EU countries where climate policies have been implemented, highlighting the effectiveness of subnational policies. Moreover, subnational analysis reveals greater variance of decoupling rates within national boundaries than between them and that countries with weaker governance typically show higher variance of decoupling within their borders. If recent rates of production-based carbon decoupling continue, less than half of subnational regions would reach net-zero before 2050, even when accounting for observed acceleration via socioeconomic development and assuming no interregional carbon leakage.
Climate change has become a global nightmare, and the awareness of the causes of carbon emissions has resulted in rigorous studies. These studies linked the increase in global warming with booming economic growth. Since global warming has become more apparent, researchers have explored ways to decouple economic activities from carbon growth. Economic and carbon growth must be decoupled to achieve a low-carbon economy to support the carbon-growth plan or emission-reduction strategy. The world is transitioning toward a carbon-neutral and green ecosystem, so finding ways to decouple carbon emissions from economic activities is an exciting topic to explore. This study reviews current information on the importance of decoupling energy from economic growth innovative techniques that thoroughly examine the challenges and constraints of low-carbon energy systems. In order to examine the detrimental effects of carbon emissions on ecosystems and the ways in which economic expansion contributes to carbon footprints, more than three hundred research papers were gathered using several search engines, including Elsevier and Google Scholar. This review revealed that decarbonization and dematerialization had been achieved without declining global economic growth. It also provides information on energy use and economic activities leading to global carbon emissions and alternative solutions to the global challenge of climate change. The decoupling methods commonly used to determine the impact of energy decarbonization on economic growth are explored. All the results suggest that economic growth is a primary mover of global carbon emission increase and must be separated to achieve a carbon environment.
The two-wheel innovation drive comprising technological innovation alongside institutional innovation serves as a crucial driving force for reducing carbon emissions and promoting the realization of dual carbon goals. Based upon the effective measurement and construction of an index system for technological innovation and institutional innovation, this paper selects panel data from 30 provinces, municipalities, and autonomous regions in China spanning from 2005 to 2019. Employing a two-way fixed effects model, it empirically examines the impact of the two-wheel innovation drive on carbon emissions in China. The findings indicate that the two-wheel innovation drive exerts a substantial inhibitory effect on carbon emissions, with evidence of single threshold effect. Specifically, there is a phenomenon of diminishing marginal benefits observed after reaching a certain level. Technological innovation, as well as institutional innovation, each demonstrate an negative effect on carbon emissions. Besides, regional heterogeneity in this inhibition effect is evident, with the most significant impact observed in the western region, followed by the central region, while the effect is relatively weaker in the eastern region. Additionally, this paper constructs an interaction term to examine the moderating effect, affirming that with economic development, the two-wheel innovation drive can more effectively achieve carbon emission reduction.
With the progress of urbanization, more activities and CO2 are concentrated in urban, and transport sector becomes one of the most important sources of carbon emissions in cities. With the quick growth of urban transport activities, to achieve China’s carbon emission peaking and neutrality target, controlling urban transport CO2 emissions becomes more important and difficult. Current studies lack a full-chain analysis of the impact of urban structure, modal share, technical level, and operational level on carbon emissions, especially the lack of analysis of rapidly urbanizing, motorizing, and electrifying in Chinese cities. To fill this gap, this paper conducts a decomposition and causal analysis of the urban transport sector in four Chinese megacities, Beijing, Shanghai, Shenzhen and Guangzhou. Base on the full chain analysis, urbanization, motorization, and technology level are the main factors influencing the growth of carbon emissions from passenger transport in the four cities. In the early stage of urban development, urbanization is responsible for the growth of carbon emissions; in the later stage of urban development, motorization is mainly responsible for the growth of carbon emissions; and in the future, technology level is the key to reducing emissions. Specifically, because of the COVID-19 epidemic, trip rates in the four cities drop significantly and make a noticeable change in CO2 emissions. Besides, it is found by the full chain analysis that the different focus on the three factors (urbanization, motorization, technological level) ultimately led to the difference in the change of carbon dioxide emissions in the four cities.
The escalating carbon emissions from global civil aviation, exceeding 70% of the International Civil Aviation Organization's (ICAO) forecasted levels between 2013 and 2019, underscore an urgent need for sustainable practices in airport terminal production. To address this challenge, the present study has formulated a multi-objective optimization model that comprehensively incorporates two crucial aspects: carbon emissions from airport terminals and passenger comfort. Specifically, models for carbon emissions and indoor environmental air quality are developed, taking into account factors such as ambient temperature, light intensity, noise, and ventilation quality concerning both airport terminal emissions and passenger comfort. Building upon these models, a multi-objective programming framework is devised, considering practical operational constraints. To contend with uncertainties in model parameters, the model is evolved into a fuzzy optimization framework and thereafter solved by the fuzzy optimization theory. The model's viability is tested through a case study of Mianyang Airport's terminal in China's southwestern region. In the context of concurrently considering carbon emission reduction and optimizing indoor environmental quality, winter carbon emissions increased by 212.09% compared to summer, while indoor environmental quality declined by 5.68%.
Several studies provide evidence that social, economic, political, and conservation-related factors impact cross-national biodiversity loss. One theoretical argument concerning biodiversity loss that has not been directly assessed involves the relationship between species loss and neoliberalism. Generally, neoliberalism promotes free markets. This has become a dominant philosophy across nations (though the strength of neoliberalism varies cross-nationally) and affects how nations interact with one another as resource users and providers. Given how neoliberal policies work to integrate nations into a single global capitalist economy, we assess neoliberalism’s effects on threatened species alongside a nation’s position in the global world-system. The current study examines nine sets of negative binomial regression models analyzing the effect of neoliberalism, world-system position, and a set of control variables on species biodiversity loss across a sample of 104 countries for which all relevant data were available. We find some support that neoliberalism increases biodiversity loss in fully assessed species (birds and amphibians). We also find support for the hypothesis that there is more biodiversity loss in the periphery and semi-periphery of the world-system (compared with core countries) in fully assessed threatened species. We argue that the results support previous research showing the sociological drivers of biodiversity loss.
The carbon dioxide (CO2) emissions caused by the global construction industry account for 36% of the world’s total carbon emissions, and 50% of China’s total carbon emissions. The carbon emissions from Jiangsu Province’s construction industry account for approximately 16% of the total emissions of the Chinese construction industry. Taking the construction industry in Jiangsu Province as our study object, therefore, this paper introduces the Intergovernmental Panel on Climate Change (IPCC) carbon emission accounting method as a means to measure the total CO2 emissions of the Jiangsu Province construction industry. Specifically, we examine the period from 2005 to 2013. Based on the Tapio decoupling model, we analyze the decoupling state between the CO2 emissions of the construction industry in Jiangsu Province and the province’s economic growth. Our paper also employs the Logarithmic Mean Divisia Index (LMDI) approach, in order to conduct a decomposition analysis of those factors that influenced the changes in the level of CO2 emissions during the studied period. According to the results of our research, during the period from 2005 to 2013, the CO2 emission levels caused by the construction industry in Jiangsu Province experienced a significant increase. The cumulative total CO2 emissions reached 402.85 million tons. During most of the years covered by our study, an expansive negative decoupling state existed between the level of CO2 emissions and the output value of Jiangsu’s construction industry. These periods were interspersed with either a weak decoupling state in some years or a strong decoupling state in other years. The indirect carbon emission intensity effect and the industry scale effect were the main factors influencing the increases in the construction industry’s CO2 emissions. At the conclusion of our paper, we put forward policy suggestions, with the objective of promoting the de-carbonization of the construction industry in Jiangsu Province.
To mitigate health and environmental effects from coal-based home heating, the Beijing Municipality has implemented a programme in 3,700 villages that subsidizes electric heat pumps and electricity, and bans coal. Here, we estimate this programme’s impact on household energy use and expenditure, well-being and indoor environmental quality by comparing treated and untreated villages in three districts that vary in socioeconomic conditions. We find that, under this programme, households in high- and middle-income districts eliminated coal use with benefits to indoor temperature, indoor air pollution and life satisfaction. In a low-income district, the policy had partial effectiveness: coal use was contingent on household wealth, and there were fewer benefits to the indoor environment and negative impacts on well-being. These results suggest that a rapid household energy transition can be effective, but it is essential to appropriately control subsidies and fine-tune supports to limit transitional hardships for the less affluent.
Purpose
Non-fossil fuels are receiving increasing attention within the context of addressing global climate challenges. Based on a review of non-fossil fuel consumption in major countries worldwide from 1985 to 2015, the purpose of this paper is to analyze trends for global non-fossil fuel consumption, share of fuel consumption and inequality.
Design/methodology/approach
The similarities were obtained between the logarithmic mean divisia index and the mean-rate-of-change index decomposition analysis methods, and a method was proposed for complete decomposition of the incremental Gini coefficient.
Findings
Empirical analysis showed that: global non-fossil fuel consumption accounts for a small share of the total energy consumption, but presents an increasing trend; the level of global non-fossil fuel consumption inequality is high but has gradually declined, which is mainly attributed to the concentration effect; inequality in global non-fossil fuel consumption is mainly due to the difference between nuclear power and hydropower consumption, but the contributions of nuclear power and hydropower to per capita non-fossil fuel consumption are declining; and population has the greatest influence on global non-fossil fuel consumption during the sampling period.
Originality/value
The main contribution of this study is its analysis of global non-fossil fuel consumption trends, disparities and driving factors. In addition, a general formula for complete index decomposition is proposed and the incremental Gini coefficient is wholly decomposed.
The argument that human society can decouple economic growth—defined as growth in Gross Domestic Product (GDP)—from growth in environmental impacts is appealing. If such decoupling is possible, it means that GDP growth is a sustainable societal goal. Here we show that the decoupling concept can be interpreted using an easily understood model of economic growth and environmental impact. The simple model is compared to historical data and modelled projections to demonstrate that growth in GDP ultimately cannot be decoupled from growth in material and energy use. It is therefore misleading to develop growth-oriented policy around the expectation that decoupling is possible. We also note that GDP is increasingly seen as a poor proxy for societal wellbeing. GDP growth is therefore a questionable societal goal. Society can sustainably improve wellbeing, including the wellbeing of its natural assets, but only by discarding GDP growth as the goal in favor of more comprehensive measures of societal wellbeing.
The United States of America is not only an important energy consuming country, but also in the dominant position of energy for many years. As one of the two largest emitters, the US has always been trying to register a decline in energy-related CO2. In order to make a further analysis of the phenomenon, we choose a new decoupling analysis with the multilevel logarithmic mean Divisia index (LMDI) method. This study examined the contribution of factors influencing energy-related carbon emissions in the United States of America during 1990–2014, quantitatively analyzed decoupling indicators of economic development and environmental situations. As is indicated in the results, economy development and activities have a significant effect in increasing carbon emission, however, measures of energy optimization such as the improvement of energy efficiency has played a crucial role in inhibiting the carbon dioxide emission. Furthermore, as is indicated in decoupling relationship, “relative decoupling” and “no decoupling” are the main states during the examined period. In order to better investigate the long-run equilibrium relationship between total carbon dioxide emissions of each effect and the relationship between CO2 emissions and economic growth, on the basis of a static decomposition analysis, we applied a dynamic analysis method-cointegration test. At last, recommendations and improvement measures aiming at the related issues were put forward.
Nowadays, BRICS (i.e., Brazil, Russia, India, China, and South Africa) becomes more and more important in the world in terms of not only economic development, but also energy consumption. The purpose of this paper is to study occurrence of a decoupling between economic growth and energy-related CO2 emission in BRICS countries from 1995 to 2014. Furthermore, the LMDI theory is utilized to find the decoupling reasons. The main conclusions drawn from the present study may be summarized as follows: (1) During the study period, China was the largest CO2 emitter among BRICS countries; per capita energy-related CO2 emission in Russia was the largest in BRICS countries. (2) The share of fossil energy to total energy consumption for South Africa accounted for more than 96 %. However, the share of fossil energy to total energy consumption for Brazil accounted for about 60 % over the study period. (3) For Brazil, Russia, and South Africa, five decoupling status occurred during the study period. However, only three decoupling status occurred in China and India. (4) The energy intensity effect played a positive role in decreasing CO2 emission in all five BRICS countries.
The growing concern of climate change has made greenhouse gas emissions, mostly as a result of anthropogenic activities, an important matter of research. The aim of the paper is to perform quantitative evaluation on the relationship between economic development and production of greenhouse gas emissions based on decoupling model theory. The paper focuses on the case of V4 countries in the period of 1991 – 2012. Throughout the more than 20 years examined, the countries spread out into many different forms of decoupling. The results of analysis suggest that in most observed partial variables occurs the strong decoupling of economic growth and greenhouse gas emissions, what can be considered as positive trend. Though decoupling elasticity convey a positive message, data indicate that, in order to meet its 2050 ambitious objectives to reduce greenhouse gas emissions, the V4 countries will need to accelerate their implementation of new policies, while restructuring the ways how they meet their demand for energy, food, transport and housing.
This study uses the Group of Twenty (G20) as a representative sample of global economic development. Within a panel Emissions–Energy–Output (EEO) framework over the period 1991–2016, to estimate the Carbon Kuznets Curve (CKC), the clean/fossil fuels energy consumption (CE/FF) elasticity of demand for carbon emissions, the causalities between emissions, energy, and economy, and thus to propose strategy for decoupling environmental pressure from economic growth (EG). The results of the descriptive statistical analysis suggest an absolute decoupling effect seems to have occurred with the drop in related environmental pressure and the continuation of economic growth. The panel cointegration test results show that there is a long-run equilibrium relationship between carbon emissions, FF, real GDP, and different types of CE [i.e., new renewable (Ren)/hydropower (Hydro)/nuclear (Nuc)]. The estimation results show that the CKC exists, and emissions are positively elastic to FF and negatively inelastic to Ren/Hydro/Nuc, although Ren's average per capita compound annual growth rate reached 14%. The results of panel vector error correction models reveal that 1) the use of nuclear energy is a key means for dealing with carbon emissions; 2) the substitutability/symbiosis exists between Ren/Hydro and FF; 3) the feedback hypothesis between EG and FF/Ren and the Hydro/Nuc-led growth hypothesis are confirmed. Therefore, energy-saving policies may hurt EG. A broad strategy for emissions, energy consumption, and economy decoupling are to support diversified, sustainable energy consumption mix (including SHP, Ren, and Nuc) and maintain stable economic growth, thereby improving energy efficiency and safety, reducing carbon dioxide intensity, and thus leading the world to absolute decoupling. Absolute decoupling is the only way to achieve a truly sustainable future.
With the limited amount of resources, developing effective strategies to make full use of them and decrease the energy consumption without too much sacrifice of economic output requires identifying key drivers of energy consumption growth rate as a prerequisite. Meanwhile, as top three consumers of primary energy of the world, China, the United States of America, and India burn over 45% of global fuels in 2016. Conducting an empirically comparative analysis of them can also set up pilot scheme for other economies to develop more efficient strategies for energy consumption. The paper modified the original Geographical Detector model with a different sampling method to detect the key driver of energy consumption growth rate, which filling the gap that there are possible interactions of potential factors. The results show that coal intensity is the biggest driver to change overall energy consumption growth rate in China and India. In comparison, for the United States, the leading drivers of energy use are the factors of individual incomes and oil intensity. In addition, all factors have interactions and enhance each other when influencing total energy consumption growth rate. India has the strongest factor interactions when influencing the energy consumption growth rate among the three economies, all interactions between factors in US is not significant as those in China and India. Besides providing outcomes that can contribute towards developing new strategies to use energy more efficiently, this research offers a pilot example of analyzing energy issues from the perspective of stratified heterogeneity in consideration the characteristic differences of each factor.
A better understanding the coupling/decoupling of economic growth from energy demand in China and India can serve both countries and other developing countries to achieve the goal of gaining higher economic growth with less energy demand. To demonstrate the relationship between coupling/decoupling economy and energy in China and India better, this work has made following contributions: (i)analyzes the decoupling elasticity between economic growth and energy consumption by using the Tapio model, (ii)explores the five key effects (labor input, investment, energy structure, energy intensity, technology level)of decoupling elasticity by combining the Cobb Douglas (C-D)function and Log-Mean Divisia Index (LMDI)method, and (iii)evaluates the decoupling efforts by applying decoupling efforts model in China and India between 1990 and 2015. The decoupling elasticity results show China's decoupling elasticity curve is in an inverted U-shaped trend, and the decoupling elastic value is concentrated between 0 and 0.8 in most years, showing a weak decoupling state. Furthermore, the decoupling state is strong decoupling during 2014–2015, indicating China has initially achieved the decoupling of economy and energy consumption. While India's decoupling elasticity presents irregular fluctuations, indicating that India's decoupling status is not very satisfactory. The decomposition results uncover that major effects of decoupling in China and India are similar, i.e., the investment effect is the biggest driver, which contributed to a cumulative increasing impact in energy consumption in China and India by 3017.10 million tonnes oil equivalent and 675.18 million tonnes oil equivalent, respectively. Whereas the energy intensity effect is the greatest inhibiting factor, which contributed to a cumulative reduction in energy consumption in China and India by 1437.81 million tonnes oil equivalent and 139.21 million tonnes oil equivalent. The decoupling efforts results show decoupling effort in China is better than that in India. Improving energy efficiency is the leading contributor to China's decoupling efforts, while advancing technology is the major contributor to India's decoupling effort.
Energy is the material basis for the development of national economic development. However, energy shortages become increasingly serious as a result of the growing population and the modernization of the global economy. Therefore, investigating drivers of energy consumption is crucial to coordinate the relationship between energy and national economic growth. This study aimed to uncover the effects of energy prices (EP), urbanization (URN) and GDP on per capita energy consumption (EC) with considering the income gaps between countries. The paper adopted the Granger causality test approach and the impulse response function analysis by using long-term time series data on EC, EP, URN and GDP during 1980–2015 in 186 countries divided into three groups (high-income, upper- and lower-middle income groups). The results indicated a long-term co-integration relationship among these variables. Granger causality test showed a bidirectional causality between URN and EC in high and lower-middle income countries but no causality in the upper-middle income group. In all groups, there is a bidirectional causal link between GDP and EC. The results of IRF showed that as urbanization level improves, its role in promoting EC become less significant. Moreover, EP affected EC negatively both in high and lower-middle income countries, but it has a positive impact on EC in upper-middle income countries. The study supports the finding that urbanization is an important factor affecting energy consumption per capita, although its contributions vary across income groups, which offers a new pathway to control the excessive growth of energy consumption.
A comprehensive understanding of the interdependence between China's economy and carbon emissions can better serve China's transition towards a low-carbon economy, and can also provide reference for other developing countries. The purpose of this paper was to investigate the dependence of China's economy on CO 2 emissions: (i) the Tapio decoupling model was applied to quantify the decoupling elasticity between China's economy and CO 2 emissions; (ii) the structure decomposition technique was utilized to quantity the contribution of China's economic structure to the decoupling elasticity; (iii) the combined decomposition model which combined Cobb–Douglas (C-D) production function with Logarithmic Mean Divisia Index (LMDI) technique and was solved the residual problem was employed to identify more driving factors (labor input, investment, carbon coefficient, energy structure, energy intensity, economic structure, and technology state) affecting the decoupling elasticity. The main results were as follows: (i) The decoupling elasticity showed a downward trend during the whole period, and the decoupling state was characterized by two types: expansive negative decoupling (2002–2005) and weak decoupling (2000–2002 and 2005–2014). (ii) The industry sector played the most negative effect on the decoupling process expect 2013–2014. From the cumulative effect, the contribution of six sectors to promote decoupling effect was ranked as follows: Construction > Agriculture > Trade > Other > Transport > Industry. (iii) The most critical factor driving the increased CO 2 emissions was the investment effect, accounting for 145.34% of the cumulative total CO 2 emissions, while the energy intensity effect contributed to a cumulative reduction in CO 2 emissions of 1443.72 Mt. Finally, some policy recommendations were offered to achieve the decoupling of China's economic growth and carbon emissions.
The experiences of peak carbon emissions in developed economies can provide a significant reference for other economies. In terms of peak carbon intensity, per capita carbon emissions and carbon emissions, the peak process in developed economies is divided into three stages. This study analyzes gross domestic product (GDP), per capita GDP, industrialization rate, urbanization rate, and other indicators in each stage. The impacts of urbanization and industrialization on carbon emissions are examined by using a threshold regression model. The results indicate that urbanization and income level have a significant double-threshold effect on carbon emissions. From the perspective of urbanization, there is no significant correlation between carbon emissions and urbanization in the low-urbanization stage. However, urbanization has a negative effect on carbon emissions in the mid-urbanization stage. Then, this inhibitory effect becomes a promotional effect when the urbanization level crosses the second threshold. From the perspective of income level, industrialization contributes to the growth of carbon emissions. The promotional effect of industrialization on carbon emissions gradually increases in the low and intermediate income levels. However, this promotional effect begins to weaken in the high income level. Our study not only extends the existing literature about peak carbon emissions, but also merits particular attention for policy makers in less developed economies.
With the growing attention on global warming, understanding the main drivers of greenhouse gas (GHG) emissions is important. This paper investigated the contribution of intrinsic reasons for consumption-based GHG emissions growth using structural decomposition analysis based on world input-output database from 1995 to 2009. The drivers are decomposed into five sub-effects at both country-level and industry-level. The results are as follows: (1) The rapid global economic growth is the dominating driving force. However, a decreasing emission intensity caused by the improvement of energy efficiency and technology innovation can contribute significantly to emission reduction; (2) Key factors contributing to GHG emissions vary in different country groups. The investment effects in developing countries are overwhelming those in developed countries. Instead, the net export effects in developed countries are greater than them in developing countries, which means that developing countries are becoming pollution haven; (3) China and India are still the key contributors of CO2 growth and CH4 growth. In developing countries, the total effects of N2O emissions changes are positive, which is mainly because agriculture plays an important role in these countries; (4) Cluster analysis depicts the relationship between growth of GHG emissions and gross domestic outputs in different countries.
Cities do play significant roles in tackling global environmental changes. Decoupling economic growth from environmental pressures is critical for global sustainable development, however, few studies pay attention to the decoupling analysis at city level. This paper conducts a comparative study of the decoupling performance of economic output from CO2 emissions between Beijing and Shanghai, the top two cities in China. In this paper, the decoupling trends and the decoupling effects are explored from a sectoral perspective, by combining the Tapio decoupling elasticity and the logarithmic mean Divisia index (LMDI) model. The results show that Beijing and Shanghai both experienced weak decoupling in construction, expansive negative decoupling in transport, expansive coupling in trade, and weak decoupling in others over the period 2000–2015. Moreover, agriculture appeared strong decoupling in Beijing, and recessive decoupling in Shanghai; industry showed strong decoupling in Beijing, and weak decoupling in Shanghai. The decoupling effects showed different magnitudes and patterns in industrial sectors between Beijing and Shanghai. Overall, per-capita GDP and population growth inhibited the decoupling, while energy intensity, industrial share, energy mix accelerated the decoupling process.
Decomposition analysis has become a popular tool to study CO2 emissions and, in this study, we developed a combined decomposition approach to emissions analysis by integrating the logarithmic mean Divisia index and production-theoretical decomposition analysis. Based on this novel approach, we investigated the driving factors of CO2 emissions in China over the latest Five-Year Plan period (2011–2015) and analyzed the inequality characteristics of such emissions. The results showed that 1) the peak value of CO2 emissions in China declined over the period; 2) the overall inequality presented a decreasing trend, whereas intragroup inequality presented a slightly increasing trend over the period; and 3) generally, the potential energy intensity effect contributed to the decrease in CO2 emissions in developed provinces, whereas the potential carbon factor effect accounted for the decrease in CO2 emissions in less-developed provinces. Based on our empirical results, we recommend that policy-makers consider several factors when implementing CO2 policies.
The world has witnessed unparalleled economic development over the past decades, but accompanied by large amount of carbon emissions, which triggered the global warming. It is critical for the global sustainable development by decoupling economic growth from carbon emissions at country level, specifically for the largest emitter, China. This study conducts a decoupling analysis from the perspective of carbon intensity (CI), per capita carbon emissions (PC) and total carbon emissions (TC) with reference to 30 Chinese provinces, covering the period of 2001–2015. Based on the Log Mean Divisa Index (LMDI) method, the effects of energy structure (ES), energy intensity (EI), economic output (EO) and population size (P) on TC at provincial level are investigated. Results show that: (1) a strong decoupling relation between GDP and CI is found in most provinces except Hainan, Qinghai and Xinjiang, while there is large room for China to decouple completely from PC and TC; (2) EO and EI are the dominated inhibiting and promoting factors respectively for carbon emission reduction; (3) ES effect on increasing carbon emission changes between positive and negative, while P has a positive but insignificant effect on the increase of carbon emissions for most provinces. The results help local governments formulate measures to coordinate regional economic development and carbon emission reduction.
China predominantly relies on thermal power generation to meet its power requirement, which has led to a major increase in total CO2 emission and poses a huge threat to the development of power industry. In order to reduce CO2 emission in power industry and develop economy simultaneously, it is necessary to study how to achieve the strong decoupling relationship between CO2 emission in power industry and GDP in China. However, such studies are relatively limited so far. Thus, this paper mainly inquiries the major driving factors on the decoupling during the period of 1985–2016. First, the decoupling state in China is quantified by using the Tapio decoupling indicator. Then, this paper carries out the decomposition of the decoupling index to explore the driving factors affecting the decoupling by the logarithmic mean divisia index (LMDI) method and Kaya identity equation. Finally, energy consumption in power generation, thermal power structure, power generation structure, transmission and distribution loss, electrification, energy intensity and economic scale are explored to discuss the decoupling relationship with respect to the CO2 emission reduction. The results show that the decoupling relationship is in an alternating state between weak decoupling and expansive negative decoupling in 1985–2016. And the influences of seven factors on the decoupling relationship are of difference. Wherein, energy intensity as well as energy consumption in power generation, promotes the decoupling while both the economic scale and electrification are the two main factors that inhibit the decoupling. The other three factors have relatively weak effects. Therefore, based on the empirical results, this paper puts forward some policy suggestions to effectively promote the decoupling between China's electric CO2 emission and economic growth.
The contradiction between economic growth and carbon emissions in China and India is the most prominent in the world. Both countries have faced tremendous pressures to curb carbon emissions, because they are major source of new added emission sources. Meanwhile, both countries have faced greater pressures to achieve industrialization and urbanization in order to eradicate poverty. Better understanding the decoupling status and its drivers can serve to develop effective policy to achieve economic growth without an increase in emission. This paper comparatively analyses the decoupling effect of the economic growth from the carbon emissions as well as its drivers during the period 1980–2014 in China and India. The Tapio decoupling model was used to analyze the decoupling status, and the co-integration theory and the impulse response functions were applied to investigate the effects of urbanization, industrialization, per capita GDP and carbon emission intensity to decoupling. The results show that China mainly performed a weak decoupling of economic growth from carbon emissions in 1980–2014, while the decoupling status of India was no regular. In China, carbon emission intensity is the biggest contributor of decoupling, followed by urbanization, per capita GDP, and industrialization. In India, the biggest driver of decoupling is also the carbon emission intensity, followed by urbanization, industrialization, and per capita GDP. Therefore, improving energy efficiency is the best policy to toward economic growth without emission growth in China and India.
Investigation of China's Carbon dioxide (CO2) emissions peaking has become a popular topic. Under the mandatory push of the national policy, exploring the challenges of achieving carbon emission peaking deserves enough attention. In this study, a hybrid method combing logarithmic mean divisia index (LMDI) and decoupling index approach, called LMDI-D, is proposed to model the challenges faced by China to achieve its CO2 emissions peaking in 2030. This problem is examined from the perspective of historical trend and future prediction. The results indicate the following: (1) The effects of economic growth are far much larger than the other inhibiting effects of energy intensity and carbon emissions coefficient on the increase of China's CO2 emissions; (2) Increment of total CO2 emission in China shows a steady downward trend under the together influence of different factors, which is good news for the “2030 target”; (3) The decoupling relationship between China's CO2 emissions and economic growth will be stronger during 2015–2030, which is the necessary condition for China to reach its “2030 target”. Overall, the goal for China's to peak its CO2 emissions in 2030 is a challenging task. Finally, several suggestions were put forward to achieve total CO2 emissions reduction in China.
Russia’s energy-related carbon emission decreased by roughly 30% between 1992 and 2017. Previous studies reported that economic recession led to carbon emission reduction in Russia during 1990s. This paper aims to examine whether the economic recession remains to lead to a decline in Russia’s carbon emission for 1992–2017. The results show that not economic recession, but improving energy efficiency is the most significant contributor to decreasing Russia’s carbon emission from 1992 to 2017. Economic recession is the major contributor to the decrease in Russian carbon emission only before the new century and then reversed to the leading contributor to the increase in carbon emission. This research also finds that a shift to less carbon-intensive fuel and decrease in population also contribute to offsetting carbon emission in Russia. Thus, this research argues that the cause for the decline in Russia’s carbon emission for 1992–2017 is not economic recession. Indeed, Russia’s economic activity and change in carbon emission have been delinked since the new century. It can be concluded that the reduction in Russia’s carbon emission during 1992–2017 arises from a combination of improving energy efficiency, a shift to less carbon-intensive fuel, and decrease in population.
Renewable energy consumption has been promoted to mitigate climate change problems under various schemes, such as the Kyoto Protocol and the Paris Agreement. A country's choice of energy resources depends on the balancing of economic growth and environmental degradation, which will be closely related to its development stage. This study examines how the relationship between renewable energy consumption and carbon emissions is associated with the development stage by applying a panel cointegration analysis to 107 countries during the period from 1990 to 2013. The analysis shows the clear differences between the groups of low- and high-income countries. For low-income countries, renewable energy consumption is positively and negatively associated with carbon emissions and output, respectively. However, for high-income countries, renewable energy consumption is negatively and positively associated with carbon emissions and output, respectively. These results have important implications for policymakers, since the discrepancies in these relationships mean that a country's renewable energy policies should be highly compatible with its development stage.
To decouple the economic growth and carbon emission has been considered imperative to promote low-carbon economy. Nevertheless, previous studies on decoupling analysis between economic growth and carbon emission were contextualized merely in individual countries rather than the globe, which are insufficient for developing the low-carbon economy as a global target. Carbon intensity (CI), carbon emission per capita (CP), and total carbon emission (TC) serve as three important indicators of the status of regional carbon emission, but only decoupling economic growth from TC was analyzed in previous studies. To close the two gaps, this study aims to investigate the global decoupling statuses of economic growth from not only TC but also CI and CP by using Tapio decoupling index. The decoupling statuses of 133 countries and income-level groups to which they are classified are identified using the data from 2000 to 2014. According to the results, it is observed that economic growth decouples from CI, CP, and TC in sequential order, which is called three-step decoupling. In the period, countries whose economic growth having decoupled from CI, CP, and TC, account for 74%, 35% and 21% respectively. Higher income-level group has the larger proportion of countries having reached their decoupling statuses. These findings may serve as valuable references for policy-makers to understand the current decoupling statuses and make three-step strategies if necessary towards the global low-carbon economy.
For the world's 20 largest emitters, we use a simple trend/cycle decomposition to provide evidence of decoupling between greenhouse gas emissions and output in richer nations, particularly in European countries, but not yet in emerging markets. If consumption-based emissions—measures that account for countries’ net emissions embodied in cross-border trade—are used, the evidence for decoupling in the richer economies gets weaker. Countries with underlying policy frameworks more supportive of renewable energy and climate change mitigation efforts tend to show greater decoupling between trend emissions and trend GDP, and for both production- and consumption-based emissions. The relationship between trend emissions and trend GDP has also become much weaker in the last two decades than in preceding decades.
China and the United States (U.S) produce approximately one-third of global economic output, and emit more than two-fifths of global total carbon emissions. Comparing the decoupling of economic growth from carbon emissions in China and the U.S. can inform the development of effective mitigation strategies for those two countries and the world. In this study, we compared both the carbon emissions performance and the decoupling performance between China and the U.S. We quantified the decoupling status in China and U.S. using the Tapio decoupling indicator, and decomposed the decoupling index to explore the driving factors affecting the decoupling using the Logarithmic Mean Divisia Index (LMDI) technique. The results show that China experienced expansive coupling and weak decoupling in most years between 2000 and 2014; the U.S. experienced mostly weak and strong decoupling. In general, income and population effects restricted decoupling, whereas the energy intensity and energy mix effects promoted the decoupling process in China and the U.S. In addition, the carbon intensity effect exerted negative and positive effects on decoupling in China and the U.S., respectively.
The development of economy in developing countries is expected to contribute mostly to the growth of world energy consumption. Using environment Impact-GDP-Technology (IGT) decoupling model, we carry out a comparative study on the decoupling trends of economic growth and energy consumption for both developed and developing countries in past five decades (1965–2015). The results indicate that the decoupling indices of developed countries are superior to that of developing countries. The specific performances are: (1) The decoupling indices of developed countries are shown to be stable and tend to approximate absolute decoupling; (2) The decoupling indices of developing countries fluctuate in the relative decoupling interval. On this basis, this research employs grey relational analysis (GRA) to explore the reasons resulting in the difference of decoupling indices from the perspective of technical progress, industrial structure and economic growth pattern. The findings show that: in developed countries, technical progress factor exerts greatest influence on decoupling indices, followed by industrial structure and economic growth pattern; in developing countries, industrial structure and economic growth pattern have greater impact on decoupling indices than technical progress. Based on research conclusion, this research offers developing countries relevant policy suggestions for energy saving and emission reduction in the future.
This study discusses decoupling trends in world economic growth and CO2 emissions based on decoupling theories. The decoupling trends of economic growth and CO2 emissions in typical developed and developing countries in 1965–2015 are compared by using an OECD decoupling factor model, and Tapio elastic analysis (TEA) method, and the IGTX decoupling model. On this basis, this study evaluates correlations of these three decoupling models by employing the Spearman's rank correlation coefficient. The research indicates that, owing to the IGTX decoupling model being easily affected by other non-environmental factors, the shorter the research period of interest, the more easily the decoupling indices distort, while the accuracy of the TEA method is not limited by the length of the research period of interest. With respect to the overall decoupling trend, the research shows that: (1) strong decoupling is found in developed countries and slightly increases in the stabilisation thereof. The decoupling state in the United Kingdom and Germany is more stable than that of the United States and France. (2) Developing countries demonstrate weak decoupling that fluctuates significantly and lacks regularity. The stabilisation and optimisation of China in decoupling process are better than those of Brazil and India. Finally, according to research conclusions, relevant policies and suggestions are proposed in view of energy savings and emissions reduction in developing countries.
Japan has achieved the decoupling of economic growth from air-pollutant emissions since the rapid decrease in air-pollutant emissions in the 1970s and the further-abatement period. Air-pollutant emissions also decreased in China recently. We analyzed the factors driving the changes in air-pollutant emission, which enabled us to identify each factor's contribution and compare the further-abatement period in Japan with the primary reduction stage in China. This study performed the Logarithmic Mean Divisia Index analysis to decompose the industrial-emission changes (SO2 and NOx) in Japan and China into the socioeconomic factors that drive these changes. Results showed that changes in these factors, especially energy intensity and economic level, contributed differently to the emission reduction in Japan and China because the two countries differed in emission-reduction periods. The decline in emission coefficient, measured by emissions per unit of energy consumption, was the most important contributor to emission reduction in Japan. The other factors did not exert considerable influence. However, energy intensity decrease in China significantly contributed to emission reduction besides emission coefficient, and economic growth had a substantial negative impact on emission reduction. Differences also appeared in contributions from the industrial and economic structure between the two countries. Some factors may have reached their limitation after the rapid reduction period and thus contributed less significantly in the further-abatement period. A tendency toward a similar condition to Japan was also observed in some factorial contributions in China. From a sectoral perspective, emission change was distributed evenly among sectors in Japan than in China. Based on the comparison between the two countries and the “new normal” that China is experiencing, we provided insights for China for further abating industrial air-pollutant emissions in succeeding years.
This paper analyzes the decoupling states between CO2 emissions and transport development in China from 1994 to 2012. The results indicate that, at the aggregate level, the Chinese transport sector is far from reaching the decoupling state. Negative decoupling or non-decoupling years account for 72.2% of the study period. At the disaggregated level, the decoupling states between CO2 emissions and eight primary fuels are as follows: raw coal and coke are in the absolute decoupling state; crude oil, gasoline and diesel are in the weak negative state; and the other three types (kerosene, heavy fuel oil, and natural gas) are in the strong negative decoupling state. Policy implications underneath the identified decoupling states are also revealed to help China build a more sustainable transportation system.
South China’s Guangdong Province, the Chinese largest provincial economy and the global 14th biggest economy, has been facing a huge challenge of achieving economic growth without emission growth. Developing new strategy for making economic growth compatible carbon reduction requires better understanding of the decoupling carbon emission from economic growth. In this paper, we conduct a comprehensive decoupling and decomposition analysis of carbon emission from economic output in Guangdong Province from a sector perspective. We firstly calculate carbon emission in six sectors based on the energy consumption of each sector and carbon coefficient of 13 types of fuels during 2000–2014, and then quantify the decoupling status between CO2 emissions and economic growth in those six sectors by using the Tapio decoupling index, finally, investigate the influencing factors of emissions by using the decomposition techniques. The modeling results show that agricultural sector has strong decoupling, industrial, transport and others sectors are weak decoupling; construction and trade sectors are expansive negative decoupling. We also find that energy intensity and economic output are the major factors influencing carbon emission, also the effects of energy structure and emission factor among six sectors are studied. Some policy recommendations finally are put forward.
This study predicts the probabilities of achieving the carbon dioxide (CO2) emission targets set by the Paris Agreement and the Intended Nationally Determined Contribution (INDC) of the top ten CO2 emitters (TTCE). The TTCE are China, USA, India, Russia, Japan, Germany, South Korea, Iran, Saudi Arabia and Indonesia based on their emission trends over 1991–2015 period. The methods of trend extrapolation and back propagation (BP) neural networks are used in this paper to overcome the weakness of multiple linear regression (MLR) and the assumptions of the environmental Kuznets curve (EKC). The results show that the model performs well and has high predictive accuracy. The volume of the CO2 emissions by the TTCE in 2030 is predicted to increase by 26.5–36.5%, compared with 2005. According to different trends of economic growth, energy consumption, and changes in share of renewable energy, the results show that China, India and Russia will achieve their INDC targets in some scenarios, whereas there will be a shortfall in achieving targets by USA, Japan, Germany, and South Korea. In particular, the carbon reduction situations of Saudi Arabia, Iran and Indonesia are quite severe. Moreover, the results show that there is no common trend that can be used as a suitable benchmark for every country for the implementation of carbon reductions targets of the Paris Agreement and their INDC goals. Finally, there are signs of improvement of the equality of carbon emissions based on the analysis of the Gini coefficient.
This study explores the driving forces of the changes of national and regional CO2 emissions using temporal decomposition analysis model, and investigates the driving forces of the differences of CO2 emissions between China's 30 regions and the national average using spatial decomposition analysis model. The changes or the differences in national and regional CO2 emissions during 2000–2014 are decomposed into nine underlying determinants. Temporal decomposition results show that economic scale effect is the dominant driving force leading to the increases in both national and regional CO2 emissions, while energy intensity effect is the main contributor to the reduction of CO2 emissions. Contribution of various variables to CO2 emissions between eastern region and central region are roughly same. Spatial decomposition results demonstrate that the differences of CO2 emissions among China's 30 regions are expanding increasingly. Economic scale effect is main driving force responsible for the difference in CO2 emissions among regions, and energy intensity effect, energy structure effect and industrial structure effect are also important factors which result in the increasing differences in regional CO2 emissions. In addition, resource-based and less developed regions have greater potential in the reduction of CO2 emissions. Understanding CO2 emissions and the driving forces of various regions is critical for developing regional mitigation strategies in China.
In this study, an Index Decomposition Analysis-Logarithmic Mean Divisia Index (IDA-LMDI) model was developed to find the drivers behind the changes in CO2 emissions between 1990 and 2012 in Colombia. The results facilitate the assessment of the impact in Colombia of the main measures regarding the mitigation of CO2 emissions. Likewise, it allows us to analyze whether the recent measures implemented by the Colombian authorities to mitigate emissions are moving in the right direction. To carry out the decomposition analysis, six effects were taken into consideration: carbonization, the substitution of fossil fuels, the penetration of renewable energy, energy intensity, wealth and population. The effects of income and population appear as drivers of emissions for the period analyzed. A stylized analysis allows richer conclusions to be extracted regarding a battery of recommendations for emission mitigation policies that are compatible with economic growth in Colombia.
In order to address climate change in an effective manner, it is essential to quantify driving forces of CO2
emissions in the fossil-fuel rich countries. Iran is among the top ten CO2 emitting countries. Moreover, it has the
largest natural gas reserve and the fourth largest oil reserve in the world. However, there is a lack of comprehensive analytical studies on quantifying the contributions of key drivers to Iran’s CO2 emissions. This study fills this gap and performs in a systematic manner three variations of decomposition analyses on driving forces of carbon emissions from 2003 to 2014 due to energy consumption of the industry, driving forces of carbon intensity of the electricity generation, and key drivers of carbon emissions due to total fossil fuel combustion. In addition, the other novelty of this study is inclusion of the effect of electricity import and export in the decomposition analysis, which opens important avenues for analysis of emissions’ driving factors in countries currently engaged, or will engage in electricity trade. In the discussion of results, we take an international perspective and discuss findings pertaining to Iran as a fuel-rich country. Furthermore, we demonstrate real applications of decomposition analysis in policy-making using real experiences of Iran. Major findings highlight that the main driver to Iran’s CO2 emissions is increased consumption, which was responsible for an additional 201.5 MtCO2 since 2004, while technology-related improvements (e.g. energy mix) were only able to offset 7.7 MtCO2. Additional natural gas capacity, especially in the transport sector helps improve the energy mix, but would require more. Insights on electricity trading and hydropower are also presented, before we end with appropriate policy implications.
Purpose
The purpose of this paper is to investigate the effects of industrialization and urbanization on CO2 emissions in 20 African countries for the period 1980 to 2013.
Design/methodology/approach
In order to correct for cross-sectional dependence, this study adopts the use of pooled mean group. Also, the study contributes to the literature by estimating the direct, indirect and total effects of industrialization and urbanization on carbon emission.
Findings
The results show that industrialization and urbanization directly increase environmental degradation. Interestingly, industrialization and urbanization were also found to reduce environmental degradation through their indirect effects on per capita income. In general, the authors conclude that the indirect effect of industrialization will overcrowd the direct effect, and this will lead to a decline in the overall effect of industrialization on carbon emission. Also, the positive direct effect of urbanization outweighs the negative indirect effect, thus the overall effect of urbanization will endanger carbon emission in the long run.
Originality/value
The existing studies on emission, industrialization and urbanization have typically been biased toward Africa. This present study filled this gap. The choice of African countries is based on the notion that the continent is desirous of expanding her industrialization level. This has coincidentally led to the increase in urbanization growth rate as well as income level of former rural dwellers. The second contribution of this study is the “effects decomposition” into direct, indirect and total effects. This is to reveal some inherent information that might be missing.
Based on a structural decomposition analysis within a multi-regional input-output analytical framework, this study analyzes and compares the changes in and geographic sources of the emissions embodied in trade (EET) at the city level. It also examines how and why the EET changed in four Chinese mega-regions between 2002 and 2010. One finding is that the geographic sources of Beijing's, Tianjin's, and Shanghai's emissions embodied in exports (EEE) are mainly located in the provinces of the Tianjin-Hebei-Henan areas and the east coast regions of China, whereas the geographic sources of Chongqing's EEE are primarily concentrated in the southeast of China and central China. Conversely, Beijing's, Tianjin's, and Shanghai's emissions embodied in imports (EEI) are strongly associated with the provinces of Northern and North Central China, whereas Chongqing's EEI are mainly related to the southeast of China and the Hebei-Henan areas. Another important finding is that a megacity's EEE are most affected by demand in the other regions and production in the local city, whereas its EEI are largely determined by demand in the local city and production in the other regions.
As a major contributor to carbon dioxide emissions, the electric power sector has instigated significant changes in environmental issues. To show the effectiveness of the program, research on whether the changes of electricity production and CO2 emissions are out of sync are conducted by applying a decoupling elasticity analysis method. Then the decoupling index from the electricity analysis on the basis of the extended multilevel LMDI method are applied to study Shandong Province, covering the period from 1995 to 2012. Finally, a comparative decoupling stability analysis is applied. Our results indicate electricity output and coal consumption play significant roles in determining levels of CO2 emissions. Also, “relative decoupling” and “no decoupling” were the main states during the study period. We also found that the decoupling index performed better (in terms of stability) than did the electricity output elasticity of CO2 emission.
Using an extended Kaya decomposition, we identify the drivers of long-run CO2 emissions since 1800 for Denmark, France, Germany, Italy, the Netherlands, Portugal, Spain, Sweden, the UK, the United States, Canada and Japan. By considering biomass and carbon-free energy sources along with fossil fuels, we are able to shed light on the effects of past and present energy transitions on CO2 emissions. We find that at low levels of income per capita, fuel switching from biomass to fossil fuels is the main contributing factor to emissions growth. As income levels increase, scale effects, especially income effects, become dominant. Technological change proves to be the main offsetting factor in the long run. Particularly in the last decades, technological change and fuel switching have become important contributors to the decrease in emissions in Europe. Our results also contrast the differentiated historical paths of CO2 emissions taken by these countries.
Since industrial sector is a leading energy consumer and CO2 emitter in China, the degree of the decoupling of CO2 emissions and industrial growth plays a critical role in realizing the energy-conservation and emission-reduction goals of China. This is the first study to present a specific investigation on the decoupling of CO2 emissions and industrial growth in China from 1993 to 2013. Using an extended logarithmic mean Divisia index (LMDI) model focusing on both energy-related and process-related CO2 emissions and introducing three novel investment factors, i.e., investment scale, investment share, and investment efficiency, we highlight and explore the remarkable role of investment in the mitigation and decoupling of CO2 emissions with industrial growth. The results show that China's industrial sector experienced the weak decoupling during 1993–2013. The investment scale is the most important factor responsible for the increase in CO2 emissions and the inhibition of the decoupling. The investment efficiency effect has a volatile trend and overall, it plays the most significant role in reducing CO2 emissions, followed by the energy intensity effect and process carbon intensity effect, whereas the energy mix, carbon coefficient, and investment share have marginal effects. Among 36 industrial sub-sectors, the seven factors of RCMCP (raw chemical materials and chemical products), NMP (nonmetal mineral products), and SPFM (smelting and pressing of ferrous metals) have significant effects on the decoupling. Thus, the three sub-sectors should be among the top concerns for abating CO2 emissions. Finally, we provide policy recommendations considering both conventional and investment factors for China's industrial sector to realize emission reduction targets.
To study the relationship between China's regional economic development and industrial carbon emission, reveal the major influencing factors and mechanism of carbon emission change in all regions, and provide theoretical evidence for governments to formulate emission reduction strategies and policies, big data analysis and the Tapio extended model were adopted to quantitatively analyse the decoupling relationship between carbon emission and economic growth in eight major regions of China between 1996 and 2012. Logarithmic mean Divisia index (LMDI) was also applied to decompose the main factors affecting carbon emission change in all regions. The results show that (1) the proportions of industrial energy related to carbon emissions in central China and the Beijing-Tianjin region decreased year by year while the opposite was the case in coastal areas of northern China. Furthermore, the proportions in other regions basically remained unchanged; (2) a weak decoupling relation between industry energy carbon emission and economic growth was found in most regions except in northwest China during the period 1996–2000, southwest China, south and north coastal regions during the period 2001–2005, Beijing and Tianjin during the period 2010–2012. Energy consumption decoupling factors exerted a consistent positive effect on industry energy carbon emission decoupling, with decoupling impact greater than emission factors; (3) the overall level of carbon emission reduction technology in China was low and backwards, and limited contributions have been made to economic growth and industry energy carbon emission decoupling. Development emphases of decoupling should be put on energy-saving technology promotion, industry structure upgrading and energy structure improvement in the future; (4) economic intensity effect has exerted the most powerful positive influence on carbon emissions, except in developed areas, and the positive effect was weaker than that in other regions.
We study the impact of US quantitative easing (QE) on both the emerging and advanced economies, estimating a global vector error-correction model (GVECM). We focus on the effects of reductions in the US term and corporate spreads. The estimated effects of QE are sizeable and vary across economies. First, we find the QE impact from reducing the US corporate spread to be more important than that from lowering the US term spread, consistent with Blinder's (2012) argument. Second, counterfactual exercises suggest that successive US QE measures might have prevented episodes of prolonged recession and deflation in the advanced economies. Third, the estimated effects on the emerging economies are diverse but generally larger than those found for the United States and other advanced economies. The estimates suggest that US monetary policy spillovers contributed to the overheating in Brazil, China and some other emerging economies in 2010 and 2011, but supported their respective recoveries in 2009 and 2012. These heterogeneous effects point to unevenly distributed benefits and costs of cross-border monetary policy spillovers.
Developing economies like Nigeria are competing strategically to ensure a rise in sustainable economic growth, and reduction in CO2 emission. The question is, could this be possible amidst the series of energy crises facing the country? It is against this development that this paper investigates empirically if the Nexus between economic growth, energy consumption, financial development, trade openness and CO2 emissions in Nigeria could provide a clue. The study used time series data from 1971to2011. To ensure a robust result, the study applied the ARDL bounds testing approach to cointegration, the Zivot–Andrew structural break test, and the Bayer–Hanck combine cointegration analysis. The causality analysis, was checked using the VECM model and this was validated using the innovative accounting and the impulse response test. The findings of the study revealed that financial development stimulates energy demand, but lowers CO2 emissions. Economic growth lowers energy demand but increases CO2 emissions. In addition to that, the study discovered how Trade openness increases energy consumption but improves Environmental quality by lowering CO2 emissions. Energy consumption was on the other hand, found to have significant increase on CO2 emissions. The Granger causality analysis revealed a bidirectional causal relationship between financial development and energy consumption, and the same inference was found in financial development and CO2 emissions. In this study, trade-led energy hypothesis and the existence of a feedback effect between economic growth and CO2 emissions were discovered. The study recommends massive investment in Nigeria’s financial sector with the motivation for these sectors to invest in efficient, and sustainable renewable energy system. How it should be done and why it should be done are carefully outlined in this study
We study changes in the aggregate carbon intensity (ACI) for electricity at the global and country levels. The ACI is defined as the energy-related CO2 emissions in electricity production divided by the electricity produced. It is a performance indicator since a decrease in its value is a desirable outcome from the environmental and climate change viewpoints. From 1990 to 2013, the ACI computed at the global level decreased only marginally. However, fairly substantial decreases were observed in many countries. This apparent anomaly arises from a geographical shift in global electricity production with countries having a high ACI increasingly taking up a larger electricity production share. It is found that globally and in most major electricity producing countries, reduction in their ACI was due mainly to improvements in the thermal efficiency of electricity generation rather than to fuel switching. Estimates of the above-mentioned effects are made using LMDI decomposition analysis. Our study reveals several challenges in reducing global CO2 emissions from the electricity production sector although technically the reduction potential for the sector is known to be great.
China has a coal addiction that has worsened over time. Its share of the world׳s total final coal consumption more than doubled over the past decade. This fast growth of coal consumption presents serious challenges to the country׳s carbon emission. In 2011, burning coal contributed 72% of China׳s CO2 emission and 19% of global CO2. A better understanding of the trajectory and drivers of Chinese coal usage will help catalyse Chinese efforts to kick its heavy addiction to coal. We analyze China׳s coal consumption during 1955–2011 using time-series data energy consumption, population, gross domestic product, and industrial production. Based on the change of its total coal consumption and coal consumption intensity, China׳s coal use for 1955–2011 is divided into six phases. Next, this paper quantifys the driving forces in each phase and drivers of coal consumption intensity, using the Logarithmic Mean Divisia Index (LMDI) technique. The result shows there is a strong positive correlation between economic growth and coal consumption, the coal intensity and economic output of second industry is the leading contributor to change of coal consumption intensity. Three policy recommendations are offered for China kicking its coal habit. This paper argues that improving coal consumption efficiency is the more optimum policy.