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An Integrated Assessment System for Regional Carbon Emissions: Insights into China’s Sustainable Development
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The relationship between economic growth and CO2 emissions has been analyzed testing the environmental Kuznets curve hypothesis, but traditional econometric methods may be flawed. An alternative method is proposed using segmented-sample regressions and implemented in 164 countries (98.34% of world population) over different periods from 1822 to 2018. Results suggest that while the association between GDP per capita and CO2 emissions per capita is weakening over time, it remains positive globally, with only some high-income countries showing a reversed association in recent years. While 49 countries have decoupled emissions from economic growth, 115 have not. Most African, American, and Asian countries have not decoupled, whereas most European and Oceanians have. These findings highlight the urgency for effective climate policies because decoupling remains unachieved on a global scale, and we are moving away from, rather than approaching, the Paris Agreement goal of limiting temperature increase to 1.5 °C above preindustrial levels.
Currently responsible for over one fifth of carbon emissions worldwide, the transportation sector will need to undergo a substantial technological transition to ensure compatibility with global climate goals. Few studies have modeled strategies to achieve zero emissions across all transportation modes, including aviation and shipping, alongside an integrated analysis of feedbacks on other sectors and environmental systems. Here, we use a global integrated assessment model to evaluate deep decarbonization scenarios for the transportation sector consistent with maintaining end-of-century warming below 1.5 °C, considering varied timelines for fossil fuel phase-out and implementation of advanced alternative technologies. We highlight the leading low carbon technologies for each transportation mode, finding that electrification contributes most to decarbonization across the sector. Biofuels and hydrogen are particularly important for aviation and shipping. Our most ambitious scenario eliminates transportation emissions by mid-century, contributing substantially to achieving climate targets but requiring rapid technological shifts with integrated impacts on fuel demands and availability and upstream energy transitions.
This study aims to explore the research trends and patterns of major issues connecting climate change and the Sustainable Development Goals (SDGs) by employing a bibliometric analysis. The study has found that there is an increasing number of research and policies in various countries committed to finding and implementing strategies to solve climate change issues. The countries with the most research in this field are China, India, the United States, the United Kingdom, and Australia, with Environmental Sciences & Ecology being the most published domain. The study has identified 19 clusters intersecting with climate change and SDGs, with the top five clusters in terms of proportion related to agricultural and food systems, water and soil resources, energy, economy, ecosystem, and sustainable management. This study also presents the trend changes of research topics intersecting climate change and SDGs every 2–3 years. Especially in the recent two years, with the convening of COP26 and COP27 and the advocacy of Net Zero and CBAM (Carbon Border Adjustment Mechanism) of the EU, important topics include renewable energy, protection of ecosystem services, life cycle assessment, food security, agriculture in Africa, sustainable management, synergies of various policies, remote sensing technology, and desertification among others. This shows an increasingly diversified range of important topics being discussed in relation to climate change and sustainable development goals.
The growing complexity of systems and problems that stakeholders from the private and public sectors have sought advice on has led systems modellers to increasingly use multimethodology and to combine multiple OR/MS methods. This includes hybrid simulation that combines two or more of the following methods: system dynamics (SD), discrete-event simulation, and agent-based models (ABM). Although a significant number of studies describe the application of hybrid simulation across different domains, research on the theoretical and practical aspects of combining simulation modelling methods, particularly the combining of SD and ABM, is still limited. Existing frameworks for combining simulation methods are high-level and lack methodological clarity and practical guidance on modelling decisions and elements specific to hybrid simulation that modellers need to consider. This paper proposes a practical framework for developing a conceptual hybrid simulation model that is built on reviews and reflections of theoretical and application literature on combining methods. The framework is then used to inform and guide the process of conceptual model building for a case study in controlling the spread of COVID-19 in care homes. In addition, reflection on the use of the framework for the case study led to refining the framework itself. This case study is also used to demonstrate how the framework informs the structural design of a hybrid simulation model and relevant modelling decisions during the conceptualisation phase.
The G20 brings together the world's major economies. Its members represent 85% of global GDP, 75% of international trade, and two-thirds of the world's population. According to the Intergovernmental Panel on Climate Change, the total remaining global carbon budget required to limit the world's temperature increase to 1.5 °C (with 67% likelihood) is 400 GtCO 2 , decreasing to 50% likelihood if emissions reach 500 GtCO 2 between 2020 and 2050. The UNFCCC's ‘Global Stocktake’ addresses the distribution of the remaining carbon budget to countries and industry sectors, to assess the technical, financial, and policy measures required for decarbonization and the national and international responsibilities involved. In this paper, the decarbonization pathways for all G20 member countries with high technical resolution, are broken down into key industry sectors. The energy-related national carbon budgets necessary to maintain the remaining global carbon budget between 400 GtCO 2 and 500 GtCO 2 are calculated and a new methodology how a fair distribution can be achieved, considering the historical emissions and economic situations of all G20 countries is presented.
Greenhouse gas (GHG) accounting of emissions from land use, land-use change, and forestry necessarily involves consideration of landscape fire. This is of particular importance for Australia given that natural and human fire is a common occurrence, and many ecosystems are adapted to fire, and require periodic burning for plant regeneration and ecological health. Landscape fire takes many forms, can be started by humans or by lightning, and can be managed or uncontrolled. We briefly review the underlying logic of greenhouse gas accounting involving landscape fire in the 2020 Australian Government GHG inventory report. The treatment of wildfire that Australia chooses to enact under the internationally agreed guidelines is based on two core assumptions (a) that effects of natural and anthropogenic fire in Australian vegetation carbon stocks are transient and they return to the pre-fire level relatively quickly, and (b) that historically and geographically anomalous wildfires in forests should be excluded from national anthropogenic emission estimates because they are beyond human control. It is now widely accepted that anthropogenic climate change is contributing to increased frequency and severity of forest fires in Australia, therefore challenging assumptions about the human agency in fire-related GHG emissions and carbon balance. Currently, the national inventory focuses on forest fires; we suggest national greenhouse gas accounting needs to provide a more detailed reporting of vegetation fires including: (a) more detailed mapping of fire severity patterns; (b) more comprehensive emission factors; (c) better growth and recovery models from different vegetation types; (d) improved understanding how fires of different severities affect carbon stocks; and (e) improved analysis of the human agency behind the causes of emissions, including ignition types and fire-weather conditions. This more comprehensive accounting of carbon emissions would provide greater incentives to improve fire management practices that reduce the frequency, severity, and extent of uncontrolled landscape fires.
Carbon emission reduction in China is of great significance to curb global warming. Based on the land use perspective, the spatial characteristics of carbon emissions and carbon sinks in 30 Chinese provinces were analyzed and the carbon balance was partitioned by combining the economic contribution coefficient (ECC) and ecological support coefficient (ESC). It was found that (1) the intensity of land use in China is strong, being high in the southeast and low in the northwest, divided by the Heihe–Tengchong Line, and carbon source and carbon sink lands show clear spatial heterogeneity. (2) Total carbon emissions show clear spatial heterogeneity. Carbon emissions from construction land are the main source of carbon emissions. The carbon emission intensity and per capita carbon emissions are both high in the north and low in the south. (3) The total carbon sink is high in the north–south and low in the middle of the country, and woodland and grassland are the main sites of terrestrial carbon absorption. The overall carbon sink intensity shows a continuous decrease from southeast to northwest. (4) Based on the ECC and ESC indicators, 30 provinces were divided into four carbon zones and differentiated low-carbon development suggestions are proposed.
Substantially enhancing carbon mitigation ambition is a crucial step towards achieving the Paris climate goal. Yet this attempt is hampered by poor knowledge on the potential cost and benefit of emission mitigation for each emitter. Here we use a global economic model to assess the mitigation costs for 27 major emitting countries and regions, and further contrast the costs against the potential benefits of mitigation valued as avoided social cost of carbon and the mitigation ambition of each region. We find a strong negative spatial correlation between cost and benefit of mitigating each ton of carbon dioxide. Meanwhile, the relative suitability of carbon mitigation, defined as the ratio of normalized benefit to normalized cost, also shows a considerable geographical mismatch with the mitigation ambition of emitters indicated in their first submitted nationally determined contributions. Our work provides important information to improve concerted climate action and formulate more efficient carbon mitigation strategies. New study finds geographical mismatch in cross-regional ranking between cost and benefit of carbon mitigation, as well as spatial mismatch between relative suitability of mitigation and mitigation ambition of emitters.
Based on the dissipative structure theory, the temporal and spatial evolution process of China’s carbon emission structure during the period of 2005–2020 is evaluated by using information entropy. The spatial correlation of information entropy of China’s carbon emission structure is measured by social network analysis , and the spatial correlation characteristics and influencing factors of information entropy of China’s carbon emission structure are discussed. The results show that the following: 1) The spatial network structure has stability and multiple overlapping additives, and the number of spatial relationships increases from 180 in 2005 to 231 in 2020. 2) According to the results of cluster sector model analysis, each province belongs to four different functional sectors respectively. The first is the “net benefit sector”, which is composed of economically developed regions such as Beijing, Shanghai, and Tianjin. The second is the “broker sector”, which includes provinces with strong economic growth vitality, such as Zhejiang, Fujian, and Guangdong. Regarding the third sector, it is the “two-way spillover sector”, which is composed of Henan, Hubei, and other fast-growing provinces in the central region. The next is the “net spillover sector”, which is composed of central and western provinces with rich resources but backward economy, such as Xinjiang, Inner Mongolia, and Shanxi. 3) The empirical results of the QAP model show that geographical adjacency, urban population, energy consumption, and R and D investment have an impact on the spatial correlation of information entropy of China’s carbon emission structure. Moreover, strengthening the spatial network correlation can promote the improvement of the carbon emission structure and be helpful to realize carbon neutrality and low-carbon sustainable development.
With the acceleration of urbanization and industrialization, carbon neutrality and peak carbon dioxide emissions have become common sustainability goals worldwide. However, there are few literature statistics and econometric analyses targeting carbon neutrality and peak carbon dioxide emissions, especially the publication trends, geographic distribution, citation literature, and research hotspots. To conduct an in-depth analysis of existing research fields and future perspectives in this research area, 1615 publications from the Web of Science Core Collection, between 2010 and 2020, were evaluated by using three analysis tools, under the framework of the bibliometrics method. These publications are distributed between the start-up (2010–2015) and the stable development (2016–2020) phases. Cluster analysis suggests three areas of ongoing research: energy-related carbon emissions, methane emissions, and energy biomass. Overall, future trends in this field include cumulative carbon emissions, the residential building sector, methane emission measurement, nitrogen fertilization, land degradation neutrality, and sciamachy satellite methane measurement. Finally, this paper further examines the most comprehensive coverage of nitrogen fertilization and the most recent research of the residential building sector. In view of the statistical clusters from 1615 publications, this paper provides new insights and perspectives for climate-environment-related researchers and policymakers. Specifically, countries could apply nitrogen fertilizer to crops according to the conditions of different regions. Additionally, experiences from developed countries could be learned from, including optimizing the energy supply structure of buildings and increasing the use of clean energy to reduce CO2 emissions from buildings.
With the statement of Chinese government on energy saving in 2020 at the United Nations General Assembly, carbon neutral was widely spread as a new concept. As a big country, China has the responsibility and obligation to make its own contribution to global climate change. This paper aims to explore and find effective ways for China to achieve carbon neutrality by 2060. We identify the main factors affecting carbon emissions by STIRPAT model; combined with the scenario analysis, we divide the year 2020 to 2060 into three stages: year 2020-2030 is carbon peak stage, year 2030-2050 is rapid emission reduction stage, and year 2050-2060 is complete carbon neutralization stage. At each stage, three development models, high, medium, and low level, were established. There are a total of 27 different scenarios in three stages. A system dynamics model was established to simulate the effects of carbon emission factors and changes in carbon sinks in different scenarios. Finally, 8 paths were found which in line with Chinese current goal of achieving carbon neutrality with treating reach carbon peak in 2030 as an additional filter condition. Comparing per capita GDP levels in different scenarios, we eventually find that keeping economic development at a low level in the first stage, a high level in the second stage, and a medium level in the finally stage, the point where net carbon emissions are less than zero for the first time will appear between year 2056 and 2057. By then, the per capita GDP will reach 144,500 yuan (based on year 2000), nearly four times 2000's. In all, these findings are helpful for policymakers to implement reasonable policies to achieve carbon emission peaking and carbon neutral in China.
Climate change is the greatest environmental threat to humans and the planet in the 21st century. Global anthropogenic greenhouse gas emissions are one of the main causes of the increasing number of extreme climate events. Cumulative carbon dioxide (CO2) emissions showed a linear relationship with cumulative temperature rise since the pre-industrial stage, and this accounts for approximately 80% of the total anthropogenic greenhouse gases. Therefore, accurate and reliable carbon emission data are the foundation and scientific basis for most emission reduction policymaking and target setting. Currently, China has made clear the ambitious goal of achieving the peak of carbon emissions by 2030 and achieving carbon neutrality by 2060. The development of a finer-grained spatiotemporal carbon emission database is urgently needed to achieve more accurate carbon emission monitoring for continuous implementation and the iterative improvement of emission reduction policies. Near-real-time carbon emission monitoring is not only a major national demand but also a scientific question at the frontier of this discipline. This article reviews existing annual-based carbon accounting methods, with a focus on the newly developed real-time carbon emission technology and its current application trends. We also present a framework for the latest near-real-time carbon emission accounting technology that can be widely used. The development of relevant data and methods will provide strong database support to the policymaking for China's “carbon neutrality” strategy. Finally, this article provides an outlook on the future of real-time carbon emission monitoring technology.
Selection of an appropriate Multiple Attribute Decision Making (MADM) method for providing a solution to a given MADM problem is always challenging endeavour. The challenge is even greater for situations where for a specific MADM problem there exist multiple MADM methods with similar degree of suitability. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and its dominant variant the Modified TOPSIS methods are two very similar methods applicable to the same type of MADM problems. This study provides extensive simulation-based comparisons and mathematical analysis of these two popular methods in order to clarify the confusion regarding their selection for solving MADM problems.
A multitude of pathways for decarbonizing energy systems have been formulated. In the development of these scenarios, the focus is often only on system costs at a given CO2 emission reduction. However, when assessing the sustainability of energy systems in a broader sense, many more aspects need to be considered: In addition to greenhouse gas emissions, energy systems induce further environmental and socio-economic impacts and must meet requirements for security of supply and cost efficiency. For assessing the compatibility of a future energy system with sustainability concepts, alternative pathways must be compared holistically using dedicated assessment methods, which is facilitated by multi-attribute decision making methods (MADM). With the target of identifying sustainable transformation pathways, we assess ten transformation scenarios for the example of Germany, using the three MADM methods weighted sum method, PROMETHEE II and TOPSIS. We find that top ranks are not completely stable across methods, but there are scenario clusters which rank high, medium and low for all methods. In the top ranks, there are both less ambitious scenarios that aim at reducing direct CO2 emissions by 80 %, and more ambitious scenarios with a reduction by at least 95 %. We conclude that scenarios with more ambitious climate protection goals are not necessarily more (or less) sustainable than scenarios that aim for a reduction of 80 % only.
Energy and emission data are crucial to climate change research and mitigation efforts. The accuracy of energy statistics is essential for mitigation strategies and evaluating the performance of low carbon energy transition efforts. This study provides the most up‐to‐date emission inventories for China and its provinces for 2018 and 2019. We also update the carbon dioxide (CO2) emission inventories of China and 30 provinces since 2012 based on the newly revised energy statistics. The inventories are compiled in a combined accounting approach of scope 1 (Intergovernmental Panel on Climate Change territorial emissions from 17 types of fossil fuel combustion and cement production by 47 socioeconomic sectors) and scope 2 (emissions from purchased electricity and heat consumption). The most recent energy revision led to an increase in reported national CO2 emissions by an average of 0.3% from 2014 to 2017. The results show that data revisions raised China's carbon intensity mitigation baseline (in 2005) by 5.1%–10.8% and thus made it more challenging to fulfill the mitigation pledges. However, the 2020 carbon intensity mitigation target was achieved ahead of schedule in 2018. A preliminary estimate of China's national emissions for 2020 shows that the COVID‐19 pandemic and lockdown was not able to offset China's annual increase in CO2 emissions. These emissions inventories provide an improved evidence base for China's policies toward net‐zero emissions.
Industrial processes are constantly changing owing to increasing demand and technological growth throughout the world. These changes influence environmental, economic, and social pillars of sustainable development to a large extent. So, production needs to be carried via considering the viewpoint of sustainable development which should include multi-criteria decision-making in planning. Including multiple criteria in planning is significant as this inclusion lessens the damaging effects on different pillars of sustainable development. Also, environmental policies are usually framed such that carbon emission need not exceed certain limit. In this paper, an optimal production mix of process routes is determined while satisfying a predefined maximum limit on total carbon emission to obtain most satisfied solution (MSS) for multiple conflicting criteria. This novel approach provides an extension to TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) methodology, and an integrated TOPSIS-pinch analysis model is proposed to determine multi-criteria-prioritized factor for ranking of new process routes in carbon-constrained production planning. The obtained MSS provides a unique solution that maximizes benefit and minimizes risk in sustainable production planning. For Iron and Steel industry, energy consumption is observed to be reduced by 74.05 × 106 GJ in comparison to the production plan given by (Sinha and Chaturvedi in J Clean Product 171:312–321, 2018), proposed method facilitates to include other objectives such as operating cost and profit. The proposed methodology is generic and can be applied to other allocation networks, an example is also discussed to show its applicability in water allocation network synthesis.
In China, numerous cities are resource-based, with substantial energy consumption, emissions, and pollution and they expand quickly and economically, notably enhancing their carbon emissions. Nevertheless, they have considerable potential for emission reduction. Assuming the “dual-carbon” goal as a backdrop, this study considered Shanxi Province, the largest coal resource-based province in China, for a case in point. It established a carbon emission system dynamics model, constructs five carbon emission systems, namely, economy, energy, population, land, and environment, and sets up four scenarios. Finally, in light of the scenario simulation’s outcomes, we explored the optimal path and policy regulations for resource-based cities to reach carbon peaks. The study conclusions show the following: (1) All factors are exhibit correlated with each other, and their influence in the four scenarios is ranked as follows: GDP, energy consumption, industrial structure, total population, land-use structure. (2) Although GDP is a key factor influencing total carbon emissions, regulating only a single factor cannot achieve the carbon emission target. Thus, all factors need to be considered and synergistically regulated to achieve optimal carbon benefits. (3) Carbon emissions are higher and grow faster in the Baseline Development Scenario and the Fast Development Scenario, particularly in the FDS scenario, where they reach 614.48 million tons. Both scenarios exceed the peak carbon target by 4.4% and 7.1%. (4) In the LowCarbon Optimization model and Resource Saving Scenario have low and slow-growing carbon emissions. Although the RSS has lower carbon emissions of 539.83 million tons, sacrificing sustainable development to reduce these emissions is unrealistic. In comparison, the LOS scenario represents the optimal path for achieving sustainable growth and lowering carbon emissions in Shanxi Province, with emissions totaling 550.99 million tons. This study implements strategies to manage the pace of population expansion, optimize the industrial structure, modify the energy structure, and optimize the allocation of land resources. The results of the study not only do our findings offer data reinforcement and implementation strategies for the low-carbon conversion of similar resource-based cities in China, but also offer case studies for different kinds of resource-based cities that fulfill the “carbon peak” objective.
Settlements and cultivated land are important production and living spaces in promoting rural revitalization. However, few studies have explored the relationship between rural settlements and cultivated land from spatiotemporal interaction perspective. This paper analyzed the spatiotemporal conversion and the interactive mechanism between rural settlements and cultivated land in a karst mountainous area (Qixingguan District of Guizhou Province) with fragile ecological environment in China during 2009–2018. The results showed that the expansion of rural settlements and the loss of cultivated land coexisted in Qixingguan District. Only 2.68% of the new cultivated land was reclaimed from rural settlements, whereas 85.45% of the new rural settlements occupied cultivated land. Six spatial expansion modes of rural settlements when occupying cultivated land were identified. Among these six modes, the area of the edge-expansion & along traffic roads (EA) mode accounted for 52.75%. The occupation by rural settlements made the cultivated land landscape more fragmented. The area ratio index of cultivated land to rural settlements (ARICR) of Qixingguan District averaged 18.75 in 2009 and 17.21 in 2018, respectively. The ARICR reduced in all township administrative regions. Cultivated land with suitable slope condition for farming or without rocky desertification was more likely to be occupied by rural settlements. The probability of cultivated land occupied by rural settlements increased with the decrease of the distance to traffic roads, towns, and old rural settlements. The better the economic and social development of the township administrative regions, the more the ARICR decreased, while the richer the agricultural resources and the better the rural development of the township administrative regions, the less the ARICR decreased. The optimal reconstruction path of rural settlements, the comprehensive conservation path of cultivated land and the urban-rural integration development path in karst mountainous area were proposed. The findings would contribute to our understanding of the spatiotemporal interaction between rural settlements and cultivated land, and would provide a theoretical basis for promoting the coordinated development of rural man-land relationship and rural revitalization in karst areas.
China's ambitious targets of peaking its Carbon dioxide (CO2) emissions on or before 2030 and achieving carbon neutrality by 2060 have been a topic of discussion in the international community. This study innovatively combines the logarithmic mean Divisia index (LMDI) decomposition method and the long-range energy alternatives planning (LEAP) model to quantitatively evaluate the CO2 emissions from energy consumption in China from 2000 to 2060. Using the Shared Socioeconomic Pathways (SSPs) framework, the study designs five scenarios to explore the impact of different development pathways on energy consumption and related carbon emissions. The LEAP model scenarios are based on the result of LMDI decomposition, which identifies the key influencing factors on CO2 emissions. The empirical findings of this study demonstrate that the energy intensity effect is the primary factor of the 14.7 % reduction in CO2 emissions observed in China from 2000 to 2020. Conversely, the economic development level effect has been the driving factor behind the increase of 50.4 % in CO2 emissions. Additionally, the urbanization effect has contributed 24.7 % to the overall change in CO2 emissions during the same period. Furthermore, the study investigates potential future trajectories of CO2 emissions in China up to 2060, based on various scenarios. The results suggest that, under the SSP1 scenarios. China's CO2 emissions would peak in 2023 and achieve carbon neutrality by 2060. However, under the SSP4 scenarios, emissions are expected to peak in 2028, and China would need to eliminate approximately 2000 Mt of additional CO2 emissions to reach carbon neutrality. In other scenarios, China is projected to be unable to meet the carbon peak and carbon neutrality goals. The conclusions drawn from this study offer valuable insights for potential policy adjustments to ensure that China could fulfill its commitment to peak carbon emissions by 2030 and achieve carbon neutrality by 2060.
Croplands, especially rice paddies, are important anthropogenic sources of methane (CH 4) emission. Currently, the effects of extreme weather events, which are occurring more frequently with climate warming, on CH 4 emissions from croplands still remain uncertain. In this study, we calibrated the CLM5BGC-Crop model and implemented it to nine cropland sites and global simulations, in order to a) analyze the spatial and temporal variations of CH 4 emissions from different crop and climate types, b) identify corresponding environmental controlling factors, and c) investigate the effects of extreme weather events on CH 4 emissions. The results show that parameterized CLM5BGC-Crop could simulate CH 4 emissions from croplands well at both site and global scales. The simulated CH 4 emissions from rice paddies (44.35 Tg CH 4 yr − 1) are 1-2 orders of magnitude higher than those from corn (0.38 Tg CH 4 yr − 1) and wheat (2.57 Tg CH 4 yr − 1) fields. Soil water content is the key factor affecting global CH 4 emissions from croplands under present and future climate. Extreme high-temperature events occurring during the rice booting-flowering stage could reduce the CH 4 emissions by 28%, while the CH 4 emissions could be increased by 12-16% when extreme high-temperature events occurred during the rice milky-maturity stage. Extreme precipitation events increased CH 4 emissions by 7% at the rice paddy site. At the global scale, extreme high temperature increased CH 4 emissions from rice paddies by 8% at middle latitudes, in contrast, extreme high temperature reduced corresponding emissions by 12% at low latitudes, but the decrease in CH 4 emissions from rice paddies at low latitudes will be reversed to a 9% increase in 2081-2100. Overall, the increasing frequency and intensity of extreme weather events in future may lead to increased global CH 4 emissions from croplands.
China's carbon emissions account for approximately a quarter of the world's total greenhouse gas emissions. In 2020, China's fossil fuels accounted for approximately 85% of the primary energy demand, with coal alone accounting for 60%. Considering the severe global warming situation, it is necessary to reveal the spatial and temporal differences and analyze the spillover effects of carbon emissions between regions. In this study, a positive and significant spatial correlation between regional carbon emissions in China was found using an exploratory spatial data analysis. The spatial Durbin model was then utilized to explore the direct and spillover effects of factors that included economic growth, the energy intensity, and the level of technological innovation on regional carbon emissions. Whether a direct effect or a spillover effect, economic growth and improvements in the regional levels of technological innovation had significant inhibitory effects on carbon emissions both in the long term and in the short term. Specifically, an increase of 1% in the level of technological innovation led to a reduction of approximately 0.17% in the region's carbon emissions. However, a growth in the energy intensity will increase carbon emissions. In addition, an increase in the technological input intensity will lead to an increase in carbon emissions in local regions. However, an increase in neighboring regions will restrain carbon emissions in a local region. Based on these findings, it is recommended that the government accelerate regional innovation synergies and increase investment in clean energy technologies.
Carbon emissions are well-established as a major contributor to global warming and other climate change effects. This work experimentally explores the influence of energy consumption, economic development, urbanization, and energy consumption on carbon emission utilizing panel cointegration tests and pooled mean group (PMG-ARDL) approaches, drawing on panel data of the Chinese country from 1995 to 2020. We add urbanization to the model to determine its significance in the interplay between GDP growth, energy consumption, and carbon emissions. The results show that urbanization has no appreciable impact on environmental quality either immediately or in the long term. Energy usage, on the other hand, was proven to considerably increase environmental harm both immediately and over time. Further research indicated that environmental distortion is a long-term effect of economic expansion for the countries studied. The key to achieving a green and clean environment is in the hands of government officials and politicians, who must pay close attention to improving appropriate energy, urban planning, and pollution reduction without slowing economic growth.
The low-carbon development of air transport industry is of great significance for China to achieve the commitment of carbon peak and carbon neutrality goals. In order to improve the basic data of aviation CO2 emissions, this study continuously collected full flight information in China from January 2017 to December 2020, and established a flight information database and an aircraft-engine parameter database. On the basis of IPCC's Tier 3B accounting method, this study established a long-term aviation CO2 emissions inventory of China from 2017 to 2020 by calculating and accumulating CO2 emissions of each flight. And aviation CO2 emissions of various provinces and cities in China were calculated combined with spatial allocation method. The results showed that aviation CO2 emissions in China was 104.1, 120.1, 136.9, and 88.3 Mt in 2017, 2018, 2019, and 2020, respectively, with annual growth rates of 15.4%, 14.0%, and −35.3% in 2018, 2019, and 2020, respectively. Affected by the COVID-19 pandemic, aviation CO2 emissions in all 31 provinces and 93% of cities decreased in 2020 compared with 2019. China is in the stage of rapid development of air transport industry, and aviation fossil energy consumption and CO2 emissions have continued to grow in recent years.
As governments around the world make more ambitious net-zero commitments, they are paying increasing attention to the greenhouse emissions embedded in traded products. Trade-related climate policy instruments – such as border carbon adjustments, linked emissions trading schemes, or certification schemes are being debated or developed in numerous jurisdictions. These instruments will rely heavily on robust and consistent calculation of embedded emissions, both for efficacy and for compliance with WTO rules. Public emissions accounting frameworks - developed for National Accounts - possess attributes of relative simplicity and modularity, which make them worthy of consideration as a starting point for development of public embedded emissions accounting to support trade-related climate policy. Using the case study of potential ammonia supply chains we assess existing methodologies of four Asia-Pacific countries against the criteria of completeness, accuracy and compliance with trade rules. We find considerable variance in practices between countries, which could be an obstacle to emergence of effective trade-related climate policy in the region. Critical areas for consideration include harmonising global warming potential (GWP) factors, and ensuring the existence of methodologies capable of estimating batch-level embedded emissions. These issues notwithstanding, public emissions accounting frameworks appear to have potential as a starting point for embedded emissions accounting to underpin trade-related climate policy.
China's CO2 reduction is crucial in mitigating global and regional climate change. Land resources used by industrial enterprises in China come from official land transactions, which indirectly affects carbon emissions. The quantitative examination of the relationship between land supply and carbon emissions can provide potential pathways and strategies for regional carbon emission reduction. For this purpose, we innovatively integrated land transaction data from China's industrial enterprise and carbon emission databases. Using this integrated database, we estimated the carbon emission changes from land transactions from 2006 to 2013, focusing on the transformation of agricultural land into industrial land in China's 331 cities. The results showed that land transactions could cause high carbon emissions in China. The industry with the largest land transaction area during the study period was the production and supply of electric power, steam, and hot water, and the one with the highest increase in carbon emissions from land transactions was the smelting and pressing of ferrous metals. By further examining the typology of the relationships between land transactions and carbon emissions for these cities, a scientific reference for pathways for reducing carbon emissions in cities was generated. Specifically, the results showed that cities with high land supply and carbon emissions were mainly resource-based or economic priority cities for development. For these cities, the entry threshold of energy-intensive industries must be raised, and the supply of land for clean energy enterprises should be increased. In contrast, cities with low land supply but high emissions primarily had depleting resources, high ecological and environmental pressure, and relatively lagging economic development. Correspondingly, the government should use the land supply to guide industries to reduce their dependence on traditional fossil energy. Regardless of the type of city in terms of relationships between carbon emissions and land transactions, indicators of carbon emission intensity should be included and carefully considered in land use planning and land transaction processes in the future.
Comprehensively clarifying China's carbon emission factors and formulating effective strategies are essential and significant for achieving the "30-60" dual carbon target. This manuscript proposed a novel hierarchical framework of multi-factor decomposition, comprehensive evaluation, prediction, and decoupling analysis of the carbon emission. The multi-factor decomposition model from the perspectives of energy, economy, and society based on the expanding the Kaya Identity and LMDI decomposition method can provide the quantification results. On this basis, this manuscript applies the entropy weight method to construct the evaluation system and generate the index from the environment, energy, and economy dimensions for China's six power generation modes. Furthermore, the carbon emission dynamics model is built based on the carbon emission data in the past 40 years and used to predict China's carbon emission in the next 40 years under multi scenarios combined with Tapio's decoupling theory. The results show that income per capita and thermal power generation result in carbon emission, while energy price and intensity are decreasing. Moreover, reducing energy consumption and increasing the proportion of renewable energy are effective ways to make China's carbon emission peak in 2030, with a peak value of 12.276 billion tons. Eventually, with policies implemented, carbon emission, economic growth, and social development are predicted to reach a strong decoupling state, indicating long-lasting negative correlations. In summary, this study will provide a comprehensive analytical solution for factor decomposition, integrated assessment, and predictive decoupling of carbon emission from a national level, aiming to provide scientifically reasonable suggestions for policies and regulations for the "dual carbon" goal.
The decoupling of CO2 emissions from power generation is of great significance for China to accomplish the commitments of carbon peak and carbon neutral. However, the related studies in China's power sector are still limited. This paper aims to explore the decoupling relationship between CO2 emissions and power generation of China's power sector as well as the driving factors of decoupling index at provincial level using Tapio model and LMDI method. The decoupling analysis shows that Heilongjiang, Beijing, Shanghai, Sichuan and Yunnan achieved decoupling and most provinces were in expansive coupling states from 2000 to 2019. The number of provinces in decoupling state during 2011–2015 was twenty-three and larger than periods of 2000–2005, 2006–2010 and 2016–2019. The decomposition analysis indicates that per capita GDP and population size were responsible for inhibiting the decoupling process for most provinces, while thermal power generation efficiency and electricity intensity promoted the decoupling. Specifically, coal-to-gas of Beijing, renewable energy utilization of Gansu and expansion in nuclear energy of Hainan contributed more to their decoupling. Besides, this paper also explores the regional agglomeration of decoupling index across provinces based on global and local Moran's I Index, demonstrating that the spatial autocorrelation was significantly positive during 2016–2019.
Global warming resulting from greenhouse gas emissions poses threats to humankind and has become a worldwide issue. As the top CO2 emitter in the world, China has committed to achieving its carbon emission peak by no later than 2030; in this context, how to best use and apply carbon emission reduction policy is particularly critical. By constructing a dynamic computable general equilibrium (CGE) model, we first examine a pure ETS included only the electricity sector in 2021, and the eight sectors starting in 2022, considering a declining carbon intensity rate of 4.5% and a higher rate of 4.8%. With the carbon intensity rates of 4.3% and 4.5%, we further evaluate two-hybrid systems of the carbon tax and carbon ETS, where the carbon tax of 10 yuan per ton is the starting levied rate in 2022 and increases at 4 yuan per ton year by year. The results proved that hybrid emission reduction policy can help reach a carbon emissions peak before 2030 and do so at a lower economic cost compared to the effect of pure carbon ETS. Besides, the coordinated use of a carbon tax and a carbon ETS can promote optimization of energy consumption structures and accelerate the decline of energy intensity and carbon intensity; this can contribute to curbing the growth of total energy consumption and total carbon emissions.
Land use is a major source of anthropogenic carbon emissions and a driver of climate change, so it is necessary to explore the spatial and temporal distribution characteristics of carbon emissions from different land use types. Based on the land use type data and fossil energy consumption data in the same period, we analyzed the spatial and temporal distribution characteristics of carbon emissions in the Yellow River Delta from 2000 to 2019 by constructing a carbon emission model, carbon footprint and Moran’s I index. The empirical results show that total net carbon emissions in the Yellow River Delta increased from 3.1×10¹⁰kg to 1.5×10¹¹kg during 2000–2019. Construction land is the main source of carbon, while forest land and water contribute more to the total carbon sink in the study area. Carbon emissions in the Yellow River Delta were spatially clustered, characterized by a larger distribution of carbon emissions in the “east-west” direction than in the “north-south” direction. The results of the study are conducive to a comprehensive understanding of the spatial distribution pattern of land use carbon sources/sinks in the Yellow River Delta, and provide a certain reference basis for the formulation of low-carbon economic policies in the region.
Mobile Fog Computing (MFC) paradigm can be integrated as a unit called as Multi-Access Edge Computing (MFC) in a fifth-generation (5G) network. There are extensive researches coercing to the MFC. Task scheduling is an important issue in the area of MFC to solve computing capacities such as limited CPU power, storage capacity, memory constraints, and limited battery life in Mobile Devices (MDs). The multi-criteria decision-making problem in fog nodes has not been widely studied. According to the variety and difficulty of criteria, the scheduling in the fog node has become a challenge. The previous works in the tasks scheduling context considered a few criteria of dynamic scheduling without covering other enough criteria. Besides, in MFC, the tasks come with different priorities. We present a scheduling algorithm based on the Priority Queue, Fuzzy and Analytical Hierarchy Process namely PQFAHP in our paper. We use PQFAHP to combine several priorities and prioritize multi-criteria. In our paper, dynamic scheduling includes the completion time, energy consumption, RAM, and deadline criteria. Our experimental results show that the proposed algorithm can consider multi-criteria for scheduling Our proposed work is one of the multi-criteria algorithms that performs optimal results than several benchmark algorithms in terms of waiting time, delay, service level, mean response time, and the number of scheduled tasks on the MFC side. This paper has considerable contributions related to the scheduling of fog computing. For instance, it could decrease 14.2%, 49%, and 26% in average waiting time, delay, and energy consumption respectively, and increase 10.8% in service level.
Carbon reduction actions may cause regions that recently entered the middle-income threshold to fall into “ecological poverty”. Identifying the factors driving industrial carbon emission costs (ICECs) growth is difficult and important for achieving “peak carbon dioxide emissions” and “carbon neutrality” goals. This study considers the northwestern provinces (NWPs) of China as a case, innovatively adopts the ecological service value (ESV) to convert the physical cost of industrial carbon emissions (PCICE) to the cost value of industrial carbon emissions (CVICE). The logarithmic mean Divisia index decomposition method is employed to analyze the impacts of the carbon emission coefficient, energy intensity, industrial structure, population size and economic factors on ICECs. Consequently, PCICE and CVICE in NWPs are increased, and CVICE is faster. The energy intensity and population size factors inhibit the increase in CVICE, and the energy intensity factor effect is stronger, the average contribution rate is in [-14.63%, −111.91%]. The carbon emission coefficient factor has a significant positive effect on CVICE, the average contribution rate is in [75.91%, 409.72%]. The economic and industrial structure factors have different effects on the direction and average contribution rate of CVICE in different provinces, the economic factor effect is obvious. The results show that the factors driving ICECs changes in middle-income regions are different. This study provides a novel theoretical framework and ideas for formulating diversified carbon emission reduction policies. It has important practical significance for different middle-income regions worldwide to formulate carbon emission reduction policies based on actual industrial economic development.
Understanding future trajectory of urban residential building carbon emissions (URBCE) is essential to seeking effective carbon-abatement pathways to combat climate change. However, future evolutionary trajectory, possible emission peaks and peaking times in this sector are still unclear. This study innovatively develops an integrated dynamic simulation model by embedding a bottom-up building end-use energy model into the system dynamics model. Based on this, scenario analysis approach is combined with Monte Carlo simulation method to explore the possible emission peaks and peaking times considering the uncertainties of impact factors. We apply the integrated SD-LEAP model to Chongqing, a typical city in China's hot-summer and cold-winter zone. Results show that URBCE will probably peak at 22.8 (±8.0) Mt CO2 in 2042 (±3.4)—well beyond China's 2030 target. Different building end-uses present substantial disparities. The contribution of combined heating and cooling to URBCE mitigation will be over 60% between business-as-usual and low-carbon scenarios. Dynamic sensitivity analysis reveals that per capita gross domestic product, carbon emission factor, and residential floor space per capita can boost emission peaks and peaking time. This study can not only boost the theory and model development for carbon emission prediction, but also assist governments to set effective carbon-reduction targets and policies.
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.