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Interplay of multiple factors behind decarbonisation of thermal electricity generation: A novel decomposition model
Abstract
Achieving carbon neutrality is crucial for the global economy to ensure sustainable development. Quantitative approaches are needed to track the emissions and determinants thereof. The Generalized Divisia Index (GDI) has received wide attention to quantify major factors driving CO 2 emissions. However, there has been no research on the application of GDI to decompose the relative variables, such as Aggregate Carbon Intensity (ACI). What is more, GDI has not been applied for spatial decomposition. To address these gaps, the present study constructs temporal-spatial GDI models to decompose the dynamics in ACI of thermal electricity generation. The case of China during 2000-2019 is considered. The results suggest that East, Central, and Northwest regions contributed to the mitigation of national ACI, whereas North and Northeast lagged behind. The temporal decomposition results imply that CO 2 emissions is the major factor causing ACI increase, whereas energy-mix promotes reduction in ACI. Energy consumption can be decoupled from ACI, but it is cumbersome to decouple CO 2 emission and electricity production from ACI. The spatial decomposition results indicate that CO 2 emission and energy consumption induce spatial differences to the highest extent.
As a crucial environmental reform system to realize “carbon peaking” and “carbon neutrality”, the pilot policy of low-carbon cities (LCCs) puts pressure and challenges on high-carbon emitting enterprises (HCEEs) while providing opportunities for these firms to take the path of independent transformation. Employing the data of Chinese listed enterprises from 2006 to 2016 and adopting a difference-in-differences (DID) model, we evaluated the impact of LCC construction on the upgrading of HCEEs and its mechanisms. The results indicate that LCC construction enhances the upgrading of HCEEs in the pilot cities. The conclusions remain stable after a series of robustness tests. The mechanism analysis reveals that LCC construction triggers the upgrading of HCEEs by promoting resource allocation efficiency, R&D investment, and green technology innovation. The heterogeneity results indicate that this positive effect is more pronounced for HCEEs in regions with more stringent environmental law enforcement. This study also observes that the upgrading impact is more prominent for state-owned enterprises, enterprises with higher bargaining power, and enterprises whose managers have a long-term vision. The above results provide directions for upgrading HCEEs and replicable evidence for cities in developing economies to fulfill the win–win target of environmental protection and economic transformation.
Promoting technological advancements and energy transitions in electricity generation are crucial for achieving carbon reduction goals. Some studies have examined the effectiveness of these measures by analysing the driving forces of “aggregate carbon intensity” (ACI) change. However, only a few studies have considered the effect of the installed capacity mix and capacity factor. Moreover, such analysis has never been applied at China’s provincial level after 2015. To alleviate this gap, our study applied a temporal and multi-regional spatial IDA-LMDI model to analyse the driving factors of ACI changes and disparities among the provinces of China from 2005 to 2019. The model notably includes the effects of the installed capacity mix, thermal capacity factor, and overall capacity factor. The analysis revealed that the decline in China’s ACI was diminished after 2015, while an ACI rebound was identified in five provinces. The changes in the ACI from 2015 to 2019 were mainly driven by the effect of the installed capacity mix rather than by the thermal efficiency and thermal capacity factor. The overall capacity factor was the only factor with a negative impact on the ACI change. We also found that its combined effect with the thermal capacity factor on increasing ACI can offset the effect of the installed capacity mix by reducing the ACI in provinces with significant additions of renewable energy installed capacity. The analysis of the influencing factors on the provincial ACI differences revealed that the share of hydropower installed capacity was significant. Moreover, the thermal efficiency and thermal capacity factor both played key roles in the ACI disparities in northeast, northwest, and central China. Overall, this study paves the way for data-driven measures of China’s carbon peak and carbon neutrality goals by improving the capacity factor of wind and solar power, leveraging the critical impact of hydropower, and narrowing the differences in the thermal power sector among provinces.
Analyzing the driving factors of PM2.5 pollution in different industries is of great significance for developing energy conservation and emission reduction policies in China's industries. In this study, the consumption-based PM2.5 emissions of China's industries are estimated by using an input–output model; on this basis, the generalized Divisia index method (GDIM) is used to measure the contributions of driving factors to the changes in PM2.5 emissions from China's six major industries. The results show that China's consumption-based PM2.5 emissions presented a downward trend from 2007 to 2015, the changes in industrial PM2.5 emissions had a much higher impact on China's total PM2.5 emissions changes than other industries and occupied a dominant position. The generalized Divisia index decomposition analysis results show that investment, output and energy consumption scale were the primary contributors to the increase of PM2.5 emissions in six sectors, with investment scale contributing the most. The investment PM2.5 emission intensity, output PM2.5 emission intensity and energy consumption PM2.5 intensity play a major role in suppressing PM2.5 emissions, while investment efficiency and energy intensity have a smaller inhibitory effect. Therefore, the government should guide investments to more high-end, low-emission industries and encourage companies to increase green investments and use renewable energy and clean energy. Avoiding excessive investments and improving investment efficiency in related industries can also effectively alleviate PM2.5 emissions.
Controlling the coronavirus pandemic is triggering a cross-border strategy by which national governments attempt to control the spread of the COVID-19 pandemic. A response based on sharing facts about millions of private movements and a call to study information behavior during the global health crisis has been advised worldwide. The present study aims to identify the technologies to control the COVID-19 and future pandemics with massive data collection from users’ mobile devices. This research undertakes a Systematic Literature Review (SLR) of the studies about the currently available methods, strategies, and actions to collect and analyze data from users’ mobile devices. In a total of 76 relevant studies, 13 technologies that are classified based on the following aspect of data and data management have been identified: (1) security; (2) destruction; (3) voluntary access; (4) time span; and (5) storage. In addition, in order to understand how these technologies can affect user privacy, 25 data points that these technologies could have access to if installed through mobile applications have been detected. The paper concludes with a discussion of important theoretical and practical implications of preserving user privacy and curbing COVID-19 infections in the global public health emergency situation.
Air quality in China is increasingly improving, but the situation facing the atmosphere environment is still dire. Regional atmospheric environmental problems characterized by PM2.5 pollutants are becoming increasingly prominent, especially in the Jing-Jin-Ji (3J) region. This study employs the generalized Divisia index approach to decompose the factors that influence the changes of PM2.5 emission in the 3J region. It is divided into 8 factors: scale effect of regional economy, scale effect of regional energy consumption, scale effect of investment in treatment of environment pollution (ITEP), technology effect of energy efficient utilization, technology effect of clean energy utilization technology, the intensity effect of regional green economic development, the intensity effect of investment in treatment of regional PM2.5 emission, and the intensity effect of regional environmental regulation. To identify the vital driving force of the change of PM2.5 emission, the contribution of each driving factor of PM2.5 emission variation is analyzed. The results show that the factors affecting the change of PM2.5 emission are almost the same, but the contribution of each factor is apparently different in the 3J region. The level of regional economic development and the scale of energy consumption promoted the increase of PM2.5 emission in the region. The growth of PM2.5 emission can be effectively controlled by green economic development intensity, energy clean utilization technology, environmental regulation intensity, and the intensity of investment in treatment of PM2.5 emission. Energy efficiency has a slight effect on the changes of PM2.5 emission. ITEP has a negative effect on the changes of PM2.5 emission. In the future, the 3J region needs to optimize the structure of ITEP further and implement the refinement and precision of pollution treatment. Moreover, it also needs to promote the development of energy clean and efficient use of technological innovation to drive PM2.5 emission reduction.
Electricity generation is the largest sector with decarbonization potential for China and the world. Based on the new emission factors, this paper aims to identify the structural and technological determinants of provincial carbon intensity in the electricity generation sector (CIE) using the multiplicative LMDI-II method. Results demonstrate that (1) China’s overall CIE decreases by 7.3% in 2001–2015, and the research period can be divided into four stages according to CIE changes (i.e., rapid growth, rapid decline, slow growth, and transition). The CIE in the 12th FYP estimated in this paper, 24.9% lower than that using the emission factors from IPCC, is closer to China’s actual situation. (2) There exists huge heterogeneity in the determinants of provincial CIE changes in four stages. CIE growth in the Northwest and Northeast is caused by the coal-dominated energy structure. CIE growth in the Southwest is attributed to the electricity structure effect, while that of the Coast region is caused by the geographic distribution effect. The electricity efficiency effect is attributed to the CIE growth for these regions and the Southwest should also place focus on the electricity trade effect. The impact of electricity trade-related factors depends on the region being a net exporter or importer of electricity. (3) To achieve carbon intensity reduction targets, 30 provinces are categorized into four types based on various combinations of structural and technological determinants. The findings provide insights into capturing future emission-mitigating focus as well as defining the emission-mitigating responsibilities between electricity exporters and importers in China.
The Chinese government has made some good achievements in reducing sulfur dioxide emissions through end-of-pipe treatment. However, in order to implement the stricter target of sulfur dioxide emission reduction during the 13th “Five-Year Plan” period, it is necessary to find a new solution as quickly as possible. Thus, it is of great practical significance to identify driving factors of regional sulfur dioxide emissions to formulate more reasonable emission reduction policies. In this paper, a distinctive decomposition approach, the generalized Divisia index method (GDIM), is employed to investigate the driving forces of regional industrial sulfur dioxide emissions in Jiangsu province and its three regions during 2004–2016. The contribution rates of each factor to emission changes are also assessed. The decomposition results demonstrate that: (i) the factors promoting the increase of industrial sulfur dioxide emissions are the economic scale effect, industrialization effect, and energy consumption effect, while technology effect, energy mix effect, sulfur efficiency effect, energy intensity effect, and industrial structure effect play a mitigating role in the emissions; (ii) energy consumption effect, energy mix effect, technology effect, sulfur efficiency effect, and industrial structure effect show special contributions in some cases; (iii) industrial structure effect and energy intensity effect need to be further optimized.
Sustainable agriculture, food security, and welfare of the farmers require an integrated analysis of the performance of the agricultural sector. In this paper, we follow the energy-environment-economy framework and focus on decomposition of changes in the energy-related GHG emission in agricultural sectors of the selected EU countries. The research relies on country-level data from FAO and Eurostat describing economic activity, energy use, and GHG emission in the agricultural sectors of the European countries during 1995-2012. The main drivers (carbon factor of energy consumed in agriculture, energy intensity of agricultural production and growth in agricultural production) and their impacts on the energy-related GHG emissions in agriculture are analysed for selected countries. The Generalized Divisia Index is applied to decompose the changes in the energy-related GHG emissions. France, Latvia, and Belgium appeared as the only countries with increase in GHG emissions during 1995-2012. In the case of France, energy intensity went up along with increase in the scale of agricultural production. In Latvia and Belgium, an increase in carbon factor appeared as the major factor driving an increase in GHG emissions. The appropriate policies need to be employed in these countries seeking to reduce GHG emissions from energy consumption in agriculture. Improvements in energy efficiency appear to be a more feasible mean for ensuring further reductions in GHG emission.
The paper uses a Spatial-Temporal decomposition approach to production to examine the relative environmental performances of eight geographic regions of China for the period 2006–2014. The study results show that the effects of the economic and energy usage efficiency are high, and their values indicate above-average performance. In terms of the effects of carbon intensity, GDP gap, and economic efficiency, the results indicate that most regions show below-average performances. Special attention should be given to the energy use production structure and product innovation to achieve a desirable level of output and the level of technical efficiency.
Exploring the intrinsic mechanism and potential evolution of carbon emissions and decoupling states are crucial to the low-carbon development of China's transportation industry. We propose an integrated analytical approach that combines the generalized Divisia index method (GDIM), Tapio, and scenario-based dynamic prediction method to broaden the research framework of low-carbon development in transportation. This proposed approach was employed to investigate the drivers for changes in the carbon emissions and decoupling status of China's transportation during 2005–2019 and the potential evolutionary trajectories during 2021–2030 under different scenarios. The empirical results show that: (1) Investment is the primary contributor to increasing carbon emissions from the transportation industry, the carbon intensity of investment is the main inhibitory factor, and these effects vary widely across the regions of China. (2) Transportation generally exhibits weak decoupling with large fluctuations. The drivers of decoupling transitions are similar to those of changes in carbon emission, but regional differences also exist. (3) The potential carbon emissions and future decoupling states of transportation differ greatly in the three scenarios, of which both peak carbon and strong decoupling states occur in the enhanced low-carbon scenario (ELS).
The achievement of China's carbon dioxide (CO2) emission reduction target is of great significance in the face of global climate change. Accurate identification of key factors that affect CO2 emissions can provide theoretical support to policymakers when designing related policies. Compared to the traditional method, the generalized Divisia index method (GDIM) can capture the influence of multiple scale factors on carbon emissions, providing new tools for studying the decomposition of carbon emissions. The article proposed a GDIM-based decomposition method to analyze the drivers that influence CO2 emissions in China from 2000 to 2017. The results indicate that investment activity is the primary element in promoting China's carbon emissions, followed by energy use and economic activities. On the contrary, investment carbon intensity is the vital inhibitory factor, followed by GDP carbon intensity. Specifically, the positive driving force of investment and energy use is gradually weakening, while the contribution of economic activities is continuously strengthening. The effectiveness of carbon emission reduction in the Northeast, East, and Southwest is actively promoting China's carbon emission reduction, while the effectiveness of CO2 emission reduction in the Northwest is not performing well. The findings provide support and reference for carbon emission control in China.
Technological Forecasting and Social Change (TFSC) is a leading peer-reviewed journal that addresses issues at the intersection of technology and society. The central strategy of the journal is to actively solicit and publish Special Issues (SIs). These SIs were first launched in 1979 to highlight and solicit manuscripts from the emerging issues of the discipline. This paper aims to analyze SIs and to highlight impact on TFSC as compared to Regular Issues (RIs). Using bibliometric analysis, this study first establishes that SIs have a higher impact on the field and the rate of citations per annum. The study then identifies leading actors (authors, affiliated institutions, and countries) and journals (knowledge inflow/outflow) that have contributed to the success of TFSC-SIs. Finally, SIs were identified. These clusters were compared with the knowledge clusters developed by Singh et al.(2020) for the entirety of TFSC journals, and four clusters unique to SIs were identified ie (Climate Change & Energy, Entrepreneurship and Innovation, Sustainability and, Social Media & Internet of Things). It is observed that these unique SI clusters have received disproportionate attention during the last decade and are likely to influence the future trajectory of the journal.
The stringency policy and economic support policy in response to and to address the coronavirus disease 2019 have become a significant concern since the end of 2019. The motivation that led to this study is that, the selection of the stringency policy and the economic support policy appear to have brought about the opposite effects of the environmental costs of carbon dioxide emissions. The study's objective is to examine the contradictory impacts of these stringency and economic support policies on carbon dioxide emissions.
This study applies panel data for the top four countries responsible for carbon dioxide emission, namely China, the United States of America, India and Russia. A fully modified ordinary least squares estimator and dynamic ordinary least squares estimator are employed to determine the long-run parameters.
The results indicate that the effect of reduced carbon dioxide emissions due to a one-unit increase in the stringency policy is greater than the effect of increased carbon dioxide emissions caused by a one-unit increase in the economic support policy. Hence, if the two policies are implemented simultaneously, a positive net effect on environmental costs will be gained.
The study investigates in a general scope, the impact these response policies have on the environment. Future researchers may enhance the research on environmental impact in different sectors due to the implementation of both policies to enrich the analytical perspective.
The results have provided implications for policymakers to emphasize more on stringency-oriented policies while giving economic support to the low-income or unemployed households in order to reduce carbon dioxide emissions.
Despite the foreseen effects of the stringency policy and economic support policy, there has hardly been any studies that have explored empirically the nexus between both policies with carbon dioxide emissions in one empirical model. Furthermore, the paper uses the high-frequency data in determining the contradictory impacts of stringency policy and economic support policy on CO 2 emissions.
For making clear decoupling state of sector economic growth from CO2 emissions in the input–output process, in this paper, we firstly explore the correlation effects of sector CO2 emissions by defining it as comprehensive effects of sector CO2 emissions from fossil energy consumption in the initial input, intermediate products, final products and circular correlation of the input–output process, and decomposing it into direct effect, full effect, Spreading effect and sensitive effect. Then we set up a methodology of correlation effects decoupling index to discover their decoupling status and sources. Finally, we concretely investigate the breakthrough points of sector CO2 emissions reduction in the case study of the Yangtze River Economic Belt (the YREB) sectors of China. The results show: (1) Correlation effects of CO2 emissions is higher in the YREB energy-intensive sectors. (2) The YREB sectors of decoupling of economic growth from CO2 emissions decreases obviously from 89.7% to 62.1% from 2007–2012 to 2013–2017, which is impacted by decoupling deterioration of correlation effects of CO2 emissions in the energy-intensive sectors. (3) The YREB key sectors of CO2 emissions reduction are the energy-intensive sectors with focus on the parts of decoupling deterioration of correlation effects of sector CO2 emissions. The proposed analysis framework is efficient for analyzing decoupling of correlation effects of CO2 emissions in the YREB sectors of China, and can also be applied to similar analysis and extended to multi-regional analysis.
Climate change caused by carbon emission threatens human well-being, and global trade drives the transfer of carbon emission between consumers and producers. Thus, quantifying global carbon transfer paths is important. Based on multi-regional input–output tables, we used structural path analysis to decompose the carbon emission processes embodied in trade to analyze the tier structure at multiple scales, and traced critical carbon transfer paths to dissect their distributions. We developed a sectoral importance indicator to consider both the amount of carbon transfer and frequency of sectors to identify key sectors. The first four tiers dominated the embodied carbon emission (>70%) at global, national, sectoral, and national-sectoral scales. Among the critical paths, basic industries (Electricity, Gas, and Water; Petroleum, Chemical and Non-Metallic Mineral Products; Transport), Construction and Services were responsible for the most embodied carbon emission, with the former having prominent direct emission, and the latter sectors having prominent carbon transfers from upstream sectors. The Construction and Electrical and Machinery in China and Services in the United States were key final production sectors; Electricity, Gas, and Water in China and the United States were key primary and final production sectors, and Petroleum and Chemical and Non-Metallic Mineral Products in China was a key primary production and reprocessing sector. Identification of key paths and sectors provides scientific guidance for formulating and adjusting global emission reduction policies.
The Chinese government has taken effective measurements to improve the health of
residents and build a healthy China, such as optimizing the energy structure and
expanding natural gas consumption. Even though a large number of studies have
explored the relationship between natural gas consumption and health, few studies
have focused on China and mostly ignored heterogeneity analysis. To assess the
effects of natural gas consumption on mortality, this study applies a two-way fixed
effect model by employing panel data from 30 provinces in China from 2001 to 2019.
Moreover, this study analyzes the differences in natural gas health benefits from the
perspective of spatial heterogeneity and energy structural heterogeneity. The results
indicate that the increase in natural gas consumption significantly reduces the mortality
rate. Specifically, an increase in 1 cubic meter in national per capita gas consumption
can reduce mortality by 0.0011 ‰, which is equivalent to 1,540 fewer deaths in 2019.
Various regions exhibit different results; expanding natural gas consumption only in
Central China and regions with large coal consumption can reduce mortality
significantly. One cubic meter increment of natural gas per capita inhibits the mortality
rate in Central China by 0.0048‰. However, the mortality rate in areas of large coal
consumption can be reduced by 0.0027‰. The relationship between natural gas
consumption and mortality is verified as linear. Finally, policy implications are proposed
for the Chinese government to accelerate the natural gas development and build a
healthy China.
Organization of Economic Cooperation and Development (OECD) economies are facing a substantial increase in the information and communication technology (ICT) investments in the context of rapid spread of the Coronavirus Disease-2019 (COVID-2019) pandemic and constraints of emissions reduction. However, the mechanism of the impact of ICT investments on carbon dioxide is still unclear. Therefore, by employing the decoupling-factor model and Generalized Divisia Index Method, we explore the decoupling states of ICT investments and emission intensity, and the driving factors of ICT investments’ scale, intensity, structure, and efficiency effects on carbon emissions in 20 OECD economies between 2000 and 2018. The results indicate that the number of economies with an ideal state of strong decoupling rose to nine between 2009 and 2018 compared to no economies between 2000 and 2009. The emission intensity of ICT investments contributes to a significant increase of carbon emissions, and the structure and efficiency of ICT investments always restrain the growth of carbon emissions. Significant emissions changes caused by the driving factors are shown in many economies before and after the crisis, reflecting the differences in the strategic choices of ICT investments and the impact on emissions due to the crisis such as the COVID-2019 pandemic. And policy implications for energy and carbon dioxide mitigation strategies in the post-COVID-2019 era are also provided.
Over the past three decades, Saudi Arabia's greenhouse gas (GHG) emissions have increased sharply. This study exposes the factors that affect GHG emissions in nine sectors via the logarithmic mean Divisia index (LMDI) method for 1990 to 2016. The analysis demonstrates that the energy effect was a leading factor increasing greenhouse gas emissions, at 386.76 million tonnes of carbon dioxide-equivalent (MTCO2e). Activity and population effects also contributed to the increase in emissions at 339.56 (MTCO2e) and 267.38 (MTCO2e), respectively, but the energy effect was greater than the other effects. Results reveal that activity, energy and population effects are greater in the electricity sector than the transport sector. The electricity sector increased greenhouse gas emissions by 4298.05 (MTCO2e) and transport, 2243.63 (MTCO2e). Therefore, policymakers need to consider climate change when they are developing economic growth plans to achieve sustainable development. This may be done through adopting a new policy to transfer from traditional sources to renewable energy sources or focusing on raising energy efficiency and changing energy structure to impact the growth of greenhouse gas emissions.
Emissions have been a critical concern for the shipping sector, calling for climate mitigation measures to be implemented in this field urgently. It is therefore useful to understand the factors that influence the relevant empirical trends so that policies can be better tailored to address the climate challenges. Given the high reliance on fossil fuels, this paper has focused on the energy consumption from international shipping, by examining 6 factors across product types and shipping routes. This is based on comprehensive microdata of almost all vessels in the world over 2014–2017. The study finds that improvements in energy intensity is consistent across product types and shipping routes, reinforcing the importance of energy efficiency as a key climate mitigation measure. However, shifts in freight transport activity across different regions have offset gains in energy efficiency and was the most dominant factor contributing to an increase in energy consumption. Monitoring of the transport structure effect is critical for progress tracking and the potential of this effect to alter energy consumption should also be factored into emission projections.
China's economy has entered the ‘New Normal’ era, transferring from the fast growth to the medium-high growth with ambitious targets on climate change mitigation and pollution alleviation. As the largest CO2 source among all industrial sectors, the electricity sector plays an important role in China's recent stagnating CO2 emissions and achieving mitigation targets. Here we estimate CO2 emissions from electricity production by using up-to-date emission factors and calculate the CO2 emissions embodied in purchased electricity using the Quasi-Input-Output (QIO) model. In addition, we quantify the drivers of CO2 emissions from electricity production and consumption using the index and structural decomposition analysis. Our results show that China's economic transition and increasing electricity intensity largely contributed to the growth of CO2 emissions in the electricity sector, while the improvement of energy efficiency, non-fossil fuel substitution, and shrinking share of industrial output reduced CO2 emissions. The consumption-based CO2 emissions in developed regions in China are higher than their production-based CO2 emissions in 12 provinces. The recent slowing down of economic growth, energy transition, improved efficiency, non-fossil fuel substitution, and electricity trade have altered the CO2 trajectory in the electricity sector since 2008 global financial crisis. This study could help policymakers better understand CO2 emissions in the electricity sector and formulate effective policies to decarbonize the electricity sector in the future.
China has set a series of periodical CO2 emission reduction targets, with a goal of peaking CO2 emission by 2030. Due to China’s large size and imbalanced development, the achievement of such targets depends on the performances of China’s provinces. Under such a circumstance, this study aims to identify the driving forces and predict the trajectories of China’s overall and provincial energy-related CO2 emissions. The generalized Divisia index decomposition analysis results show that investment sale expansion is the major driver of CO2 emission growth in China and most Chinese provinces, which is partly offset by the efficiency gains. Our dynamic scenario simulation results show that most Chinese provinces will achieve their CO2 intensity reduction targets but have difficulties in achieving their CO2 emission peak targets. Further integration of both efficiency and technology improvement is one effective approach toward peaking CO2 emissions. Provincial disparities exist, resulting in that different factors have different impacts in different Chinese provinces. Consequently, it is crucial to set up appropriate province-specific emission peak targets and raise province-specific emission reduction policies by considering the local realities.
Production-theoretical decomposition analysis (PDA) is widely used to explore the factors related to changes in carbon emissions. However, traditional PDA mainly focuses on scenarios of decision making units (DMUs) with constant returns to scale, and neglects technology heterogeneity among DMUs. Therefore, this paper proposes a joint approach combined PDA and index decomposition analysis (IDA) based on the variable returns to scale (VRS) and the meta-frontier model, and further decomposed the change of carbon emissions into 9 driving factors. Based on above, we investigated the effects of the nine factors on the change of carbon emissions at national, regional and provincial levels in China during the period of 2007–2016. The results indicated that the increase of carbon emissions in China have slowed down since 2012, but the overall situation is not optimistic, especially in the western region. Economic activity played a dominant role in increasing carbon emissions for each region. Carbon emission efficiency and potential carbon factor were also the two important factors related to increase carbon emissions. Conversely, energy intensity, the catch-up effect of pure technical efficiency and scale efficiency on carbon emissions played a positive role in decreasing CO2 emissions, especially for the eastern region. However, the driving factors related to energy usage, such as the catch-up effect of energy usage scale efficiency and the pure energy usage technical efficiency had trifling and unstable effects on the change of carbon emissions. Based on the above analysis, we propose suggestions to reduce carbon emissions.
The International Energy Agency (IEA) pointed out that thermal electricity generation was the largest contributor to the CO2 emissions in 2018, particularly in China. Inequality and spatial decomposition analyses have been extensively applied to monitor CO2 emissions and climate change. However, there have been no studies focusing on the inequality of CO2 emissions or aggregate carbon intensity (ACI) in the electricity sector. Furthermore, inequality analysis and spatial analysis have not been combined to analyze the ACI in the electricity sector. This study adopts the Theil index to analyze the intraregional and interregional inequality of provincial ACI in China’s thermal electricity sector. In addition, the nested spatial decomposition approach is proposed to isolate the factors behind variation in the ACI at the regional and provincial levels. The ACI of most provincial thermal electricity generation grids declined substantially during 2000–2016. By 2016, half of the provinces had achieved the 2020 ACI reduction target of 865 gCO2/kWh ahead of schedule. Decomposition based on the Theil index demonstrated that intraregional inequality was the major factor causing the overall inequality of ACI, especially within the North and South regions. The spatial decomposition indicates that energy intensity is the most significant factor behind the decrease in ACI followed by energy structure. The emission coefficient exerted limited impact across all the six regions. Besides, Northwest rose in the rankings of based on the energy intensity, energy structure, and ACI during 2000–2016, whereas North and Northeast saw the opposite pattern. Policy implications based on empirical research are provided to further govern the ACI and curb CO2 emissions.
Even though renewable energy development has gained momentum in China, thermal power generation still accounts for approximately 70% of the county's total power generation serving as the major source of carbon dioxide (CO2) emissions in China. Facing the challenges of meeting 2030 peak target of CO2 emission and realizing the coordinated development of thermal power generation in Beijing-Tianjin-Hebei region, this paper applies generalized Divisia Index Method (GDIM) to decompose the dynamics in the relevant CO2 emission. The effects of five factors including electricity demand, energy consumption, technology, energy efficiency and energy-mix are considered. The decomposition suggests that electricity demand is the primary factor driving the CO2 emission up, whereas technology effect decreases CO2 emission the most. Given the significant roles of technology, energy-mix and energy efficiency in CO2 emissions reduction, seven scenarios are designed to identify the optimal coordinated development pathway for thermal power generation in Beijing-Tianjin-Hebei region. Through upgrading energy structure and/or enhancing energy efficiency, the thermal power generation in Beijing-Tianjin-Hebei region can achieve coordinated development and realize the 2030 peak target under four scenarios. The detailed development pathways for CO2 emissions and specific policy implications for Beijing, Tianjin and Hebei are provided to further govern CO2 emissions and maintain sustainable development.
Even though Chinese government has been promoting the development of renewable energy, coal-fired thermal electricity generation still accounts for nearly 70% of the total electricity generation, proving to be the largest carbon dioxide (CO 2 ) emitter in China. Uncovering the driving forces of CO 2 emissions, thus, is of great significance to provide appropriate mitigation policies for the sustainable development of China's thermal electricity generation. In this regard, this study aims to fill a research gap by applying Index Decomposition Analysis (IDA) approach via the Generalized Divisia Index Model (GDIM) to examine the driving factors behind the CO 2 emission changes in China's thermal electricity generation during 2000–2016. The decomposition results indicate that the factors contributing to the growth in CO 2 emission can be ranked as follows: economic activity (52.0%), electricity demand (45.8%) and energy use (36.2%), whereas factors suppressing the growth in the mission are carbon intensity change (−17.7%), technology (−11.3%), energy mix (−2.4%), energy efficiency (−1.7%) and electricity efficiency (−0.9%). Noteworthy, the promoting effect of the economic activity varied little with time, whereas that of electricity demand and energy use exhibited a downward trend in general. Besides, though the progress in technology contributed a lot to the decrease of CO 2 emission, its decreasing effects tended to diminish since 2013. Northeast and East regions appeared as those contributing to the mitigation of the CO 2 emissions from China's thermal electricity generation, whereas the North and Northwest regions exerted a lag to the abatement of CO 2 emission.
Logarithmic mean Divisia for index decomposition analysis (IDA-LMDI) has been applied to evaluate the aggregate carbon emission intensity of electricity (ACI) evolution considering explaining factors as fuel mix, thermal efficiency, fossil share and geographical effects. However, the capacity factor of the generation system has not been duly considered in previous LMDI formulations. The capacity factor provides a perception of the time of use of the generation infrastructure. Despite LMDI analysis have been widely applied to explain the electricity-related CO2 emissions changes in many countries and regions of the world, Latin America & the Caribbean (LAC) power sector has not been analyzed yet. Since 1990 the global ACI declined around 5% whereas the ACI of LAC is just going in the opposite direction with a significant increase of 10%. To fill the research gap, this paper presents a new and general temporal IDA-LMDI formulation in order to expressly include the effect of capacity factors and analyze the evolution of LAC's ACI between 1990 and 2015. Results reveal the increase of the ACI in the region is due to structural reasons, mainly in Brazil. Intensity factors as thermal efficiency and fossil mix were also substantially improved, mainly in Mexico, but not enough to cut down the regional ACI increase. As a key result, it is shown that the capacity factors of fossil-based generation –mainly in Venezuela, Mexico and Brazil– are a relevant driving force behind the ACI increase. As Brazil is the largest producer and its capacity factor is relatively low compared to other LAC countries, the increasingly dispatch of fuel-fired power plants to cover base-load may boost the ACI of the region in the future jeopardizing the compliance of the climate goals.
To reduce carbon intensity efficiently, it is important to identify regional differences of China's carbon intensity considering the decoupling relationship between economic output and carbon emissions. This paper divided China's 30 provinces into 4 regions based on the relationship between the average annual growth rates of CO2 emissions and GDP, and explored the driving forces of carbon intensity in 2000 and 2015 for China's 30 provinces and 4 regions This decomposition method can provide the rankings and specific drivers of carbon intensity, including energy intensity, industrial structure and energy structure. The results show that during 2000–2015, all provinces' carbon intensity decreased except for Hainan. Ningxia, Shanxi, Guizhou, Qinghai and Xinjiang were the five top provincial drivers of the carbon intensity growth in China. Energy intensity was the most important driving factor influencing the change of carbon intensity. Industrial structure and energy structure had small effects on the change of carbon intensity. Moreover, the region III with a high CO2 emission growth rate and a low GDP growth rate had a poor performance of carbon intensity, due to the large impact of energy intensity. Based on the results of this study, the reduction in carbon intensity could be achieved by further reducing energy intensity, optimizing energy and industrial structure, especially in the provinces with fast CO2 growth rates and slow GDP growth rates.
The quantification of drivers of CO2 emissions from the electricity generation sector is an area that has attracted a great deal of attention among researchers and policy makers. Index decomposition analysis is a technique that has been widely used for this purpose. The increasing interest, however, has led to the usage of a variety of formulae in the decomposition. In this paper, a literature survey on the related publications is first compiled. Based on the survey, key features of studies and development trends are discussed. The decomposition formulae used are systematically analysed to elucidate the differences and relationships among them. An approach which quantifies the share of fossil fuels in the electricity mix and considers the generation efficiency by fuel type is recommended as it leads to the most comprehensive and meaningful decomposition results. Further extensions to the current practice to include factors such as transmission and distribution losses as well as carbon capture and storage are also discussed. The findings and recommendations provide insights into the analysis of CO2 emissions from the electricity generation sector where major structural changes are expected to take place in future.
Despite the signing of the Paris climate agreement, there is still great uncertainty regarding the world's ability to decarbonize and meet the 2 °C target. In this regard, the electricity production sector deserves particular attention. The sector has the largest decarbonization potential and its share of the world's CO2 emissions from fuel combustion increased from 30% in 1990 to 36% in 2014. To better understand global trends, this study analyses the factors influencing changes in the global aggregate carbon intensity (ACI) of electricity, a measure of the level of CO2 emissions per unit of electricity produced, over the last 25 years using multidimensional index decomposition analysis. It finds that global ACI barely improved since 1990 because of a shift in electricity production from developed to developing countries with higher ACIs. This geographical shift offset consistent improvements to power generation efficiency worldwide and is likely to persist in the future. To keep the 2 °C target realisable, it is imperative to enhance international cooperation to lower the ACIs of emerging economies and deepen the penetration of renewables, which have thus far performed below expectations.
China's power generation accounted for 24% of the world's total output, ranking first in 2015. As a suitable performance indicator, carbon intensity of electricity reflects a variation in the average level of carbon emissions per unit of electricity generated. As the largest carbon emitter among various industries, power sector is in duty bound to reduce carbon emissions and achieve sustainable development. Great progress has been made in China's power sector, which is reflected by the effects of technological innovation and structural adjustment. With logarithm transformation and integral transformation, three-dimensional decomposition model is proposed to quantitatively measure the changed carbon intensity of electricity due to technological innovation and structural adjustment. In the context of three-dimensional model, the variation of carbon intensity can be decomposed into absolute changes and/or relative changes. Furthermore, accumulative decomposition results are obtained by chain linked calculation rules. Finally, the proposed models are implemented in China's power industry to reveal its historical progress during 1980–2014. Some important conclusions are also found based on the decomposition results.
Index decomposition analysis (IDA) has been widely applied to study CO2 emissions from electricity generation. However, most have focused on emissions at the country level, less attention has been given to emissions at the regional level. To fill the gap, this study firstly utilized a Logarithmic Mean Divisia Index (LMDI) method to analyze the driving forces of aggregate carbon intensity (ACI) of electricity generation in China from 2000 to 2014. A regional attribution analysis was introduced to look into the contributions from 30 provinces to the driving forces. Then, a multi-regional spatial-IDA was further adopted to assess the emission performance of electricity generation in 30 provinces. The results of temporal-IDA and regional attribution analysis show that the ACI in China dropped notably by 14.5% from 2000 to 2014. Thermal efficiency improvement was a major driver for the decrease, due largely to the significant improvement in thermal generation efficiency in the eastern coastal regions. Clean power penetration reduced ACI remarkably as well, of which the western regions were the main contributors. The spatial-IDA results indicate that the emission performance of electricity generation in different regions varied significantly. While the western regions performed better in clean power penetration, the eastern regions performed better in thermal generation efficiency. Based on the findings, several regional policy strategies were recommended to further lower down ACI of electricity in China.
A major share of China's total carbon dioxide (CO2) emissions is from the electric power sector. In 2010, almost 40% of all CO2 emissions in China were from this sector. This is because the country predominantly depends on thermal electricity generation to meet its power requirement. This study analyses the CO2 emissions from thermal electricity generation in China between 2000 and 2012 at the regional grid level. Logarithmic Mean Divisia Index methodology is employed to identify the factors that influence changes in CO2 emissions over time. This study also predicts China's energy consumption and CO2 emissions patterns between 2013 and 2020, forecasting rates of increase in energy consumption across six regional grids from 0.9% to 9.7%. CO2 emissions related to thermal electricity generation increased from 981.33 million tons (Mt) in 2000 to 3342.79 Mt in 2012, which is an annual growth rate of 10.75%. These increases are not aligned with China's commitments on reducing emissions to the Asia-Pacific Economic Cooperation forum. China's CO2 emissions are forecasted to increase to 5596 Mt by 2020 if the current increasing trends is not effectively curbed after 2012.
There has been growing interest among researchers and policymakers in comparing or benchmarking countries on the basis of their performance in energy consumption or energy-related CO2 emissions. Such studies allow variations among countries to be revealed, the contributing factors identified, and the scope for improvement better understood. At the same time, tracking changes or quantifying improvements in energy use or emissions over time in a country have long been a focus area of researchers and policy makers. To provide a fuller picture on country performance in a multi-country study over time, it would be of interest to integrate the above-mentioned spatial and temporal analyses in a single analysis framework. This paper deals with this issue using the technique of index decomposition analysis. A spatial–temporal approach is introduced and two application cases are presented to illustrate how the approach can be applied. The first analyzes variations and changes in the aggregate CO2 intensity of electricity production for ten countries from 1990 to 2010, and the second deals with variations and changes in the aggregate energy intensity for eight economic regions of China from 2002 to 2012. In addition, two different ways of presenting the results are introduced. Our study shows that the proposed approach can supplement studies which are conducted purely on a spatial or temporal basis.
The Association of Southeast Asian Nations (ASEAN), with its ten member countries, has a total population exceeding 600 million. Its energy-related CO2 emissions have been growing and in 2013 amounted to 3.6% of total global emissions. About 40% of ASEAN's energy-related CO2 emissions are currently attributable to electricity production. In view of this high share, we study the CO2 emissions of ASEAN's electricity production sector with a focus on the aggregate emission intensity (ACI) given by the level of CO2 emissions for each unit of electricity produced. Drivers of ACI are analysed for individual countries and spatial analysis is conducted by comparing factors contributing to differences between the ACIs of individual countries and that of the ASEAN average. Arising from these analyses and in light of the current developments, it is concluded that drastic actions need to be taken both at the national and regional levels in order to reduce growth in the region's electricity-related CO2 emissions. Two key policy issues, namely overcoming national circumstances to improve electricity generation mix and improving power generation efficiency, are further discussed.
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 proposed its ambitious cap targets of carbon emissions in both carbon intensity (CO2 emissions per unit of GDP) and carbon scale (gross carbon emissions). Since mining sector is the foundation of the whole industrial production as well as a carbon intensive sector, it is critical to uncover the key driving factors on inducing corresponding carbon emissions so that appropriate mitigation policies can be raised. Under such a circumstance, this paper aims to fill such a research gap by employing a novel index decomposition method, namely, Generalized Divisia Index Method (GDIM), so that the driving factors of energy-related carbon emissions changes in China’s mining sector and its five sub-sectors over the period of 1999–2013 can be identified. In addition, a scenario analysis approach is applied in order to seek the feasible mitigation pathways on China’s mining sector and its five sub-sectors. The results indicate that output scale effect is the primary contributor of the increase in carbon emissions of both mining sector and its five sub-sectors and energy use effect also plays a positive role, while carbon intensity effect contributes most to the decrease in carbon emissions. All sub-sectors have achieved the target of 45% carbon intensity reduction except the extraction industry of petroleum and natural gas. Nevertheless, more efforts should be made for the whole mining sector in order to achieve the 2030 peak target of carbon scale.
Index decomposition analysis (IDA) was first extended from energy consumption to energy-related CO2 emission studies in 1991. Since then many studies have been reported covering various countries and emission sectors. However, unlike the case of energy consumption studies, a comprehensive literature survey that focuses specifically on emission studies has so far not been reported. In this paper, we attempt to fill this gap by reviewing 80 papers appearing in peer-reviewed journals from 1991 to 2012 in this application area. The first part of this paper deals with the developments with regard to the IDA approaches used by researchers, and the scope and focus of their studies. In the second part, the empirical results reported in the surveyed studies are analyzed, consolidated, and presented by emission sector. The objective is to reveal the relative contributions of key effects on changes in the aggregate carbon intensity, and this is done by emission sector and by country. The findings of both parts are useful in understanding the development of IDA in the application area of emission study, as well as the key drivers of aggregate carbon intensities in the past and their possible future developments.
Index decomposition analysis (IDA) is a popular tool for studying changes in energy consumption over time in a country or region. This specific application of IDA, which may be called temporal decomposition analysis, has been extended by researchers and analysts to study variations in energy consumption or energy efficiency between countries or regions, i.e. spatial decomposition analysis. In spatial decomposition analysis, the main objective is often to understand the relative contributions of overall activity level, activity structure, and energy intensity in explaining differences in total energy consumption between two countries or regions. We review the literature of spatial decomposition analysis, investigate the methodological issues, and propose a spatial decomposition analysis framework for multi-region comparisons. A key feature of the proposed framework is that it passes the circularity test and provides consistent results for multi-region comparisons. A case study in which 30 regions in China are compared and ranked based on their performance in energy consumption is presented.
This paper introduces a novel approach to estimating the impact of the major factors driving CO2 emissions: GDP, energy consumption, population, their carbon intensities, and other related indicators that may be chosen arbitrarily. The suggested approach is based on the generalization of the Divisia index to interconnected factors. The approach also extends the Kaya identity by explicitly including Gross Domestic Product and energy consumption. A computer program in R language aimed at automating the calculations is supplied. Factorial analysis of the United States’ and China's CO2 emissions is provided as an example of application.
Substantial investments are expected in the Indian power sector under the flexibility mechanisms (CDM/JI) laid down in Article 12 of the Kyoto Protocol. In this context it is important to evolve a detailed framework for baseline construction in the power sector so as to incorporate the major factors that would affect the baseline values directly or indirectly. It is also important to establish carbon coefficients from electricity generation to help consider accurate project boundaries for numerous electricity conservation and DSM schemes. The objective of this paper is to provide (i) time series estimates of indirect carbon emissions per unit of power consumption (which can also be thought of as emission coefficient of power consumption) and (ii) baseline emissions for the power sector till 2015. Annual time series data on Indian electricity generating industry, for 1974–1998, has been used to develop emission projections till 2015. The impacts of generation mix, fuel efficiency, transmission and distribution losses and auxiliary consumption are studied in a Divisia decomposition framework and their possible future impacts on baseline emissions are studied through three scenarios of growth in power consumption. The study also estimates and projects the carbon emission coefficient per unit of final consumption of electricity that can be used for conducting cost benefit of emission reduction potential for several electricity conserving technologies and benchmarking policy models.
A boundary problem in energy-related carbon emission decomposition that has implications on energy policy is analyzed. It concerns the coverage of energy sources in CO2 emission analysis or, more specifically, the treatment of energy sources such as nuclear and hydro that do not emit CO2. A case study related to the decomposition of CO2 emissions in electricity generation in Korea is presented to illustrate the issues involved.
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