Increase In Carbon Emissions Research Articles

Nexus between resource policy, renewable energy policy and export diversification: Asymmetric study of environment quality towards sustainable development

This study examines the environmental performance of the United Arab Emirates (UAE) towards its sustainable development amidst its heavy endowment with natural resources and export diversification. The UAE is among the economies that depend on both export diversification and natural resources for their sustainable economic development. The two mentioned indices (export diversification and natural resources) have been proven to be among the drivers of carbon emissions. Notwithstanding, the UAE has a desert environment with nearly continuous sunlight capable of generating and boosting its renewable energy development. With this potential, it is crucial to create a green energy strategy since renewable energy sources are gaining popularity as alternatives to conventional energy sources. Natural resources and export diversification have garnered mixed reactions in their role towards environmental development. While some researchers report that they induce degradation, others suggest that they mitigate environmental damage. Also, empirically, studies have found renewables such as hydroelectricity, wind and geothermal energy capable of moderating and mitigating the negative impact of carbon emissions on the environment. In this study, data from 1990 to 2019 is analyzed using the nonlinear autoregressive distributed lag (NARDL) and dynamic ordinary least squares (DOLS) scientific methods. The variables of interest are natural resources, export diversification and renewable energy. Findings from NARDL show that export diversification, natural resources and economic growth negatively impact the environment in the UAE by inducing carbon emissions. Specifically, positive and negative shocks of export diversification, natural resources and economic growth to carbon emissions increase and decrease carbon emissions, while positive and negative shocks of renewable energy to carbon emissions decrease and increase carbon emissions. This indicates that the UAE has the potential to achieve its carbon neutrality target with a renewable energy policy.

Impact of Economic and Environmental Dynamics on Human Development: An Analysis of HDI Classifications

The whole world is facing the issue of altering climate and bad environmental quality due to the extreme increase in carbon emissions. Against the backdrop, this study plans to examine the effect of environmental degradation on human development, controlling for income inequality, education inequality, gender inequality, unemployment, GDPPC, institutional quality and urbanization in the context of very high, high, medium and low HDI economies during the time scope from 1996 to 2021. For measuring environmental impact, we use both ecological footprints and carbon dioxide emission (CO2) figures for these countries. Overall, the generalized method of moments analysis confirms the strong relationship among the study variables. GMM analysis confirms that environmental quality by surging the ecological footprints and carbon emission deteriorates human development of the selected nations. Similarly, disparities in the form of income, education and gender impose an adverse impact on human development as unemployment also has a contrary impact on human development. While GDP, institutional quality and urbanization are evidenced to ensure human welfare. Hence, considering these key outcomes, a set of relevant human welfare-related policies needs to be recommended in the context of selected countries.

The nonlinear relationship between resource endowments and carbon emissions: threshold effects of marketization degree and urban services agglomeration

ABSTRACT With increasing carbon emissions, previous studies surrounding the relationship between resources and carbon emissions mainly focus on the linear framework. However, there are countries with high resource endowments and low carbon emissions, which can’t be explained by the linear framework. Therefore, this study revisits this issue from the perspective of curvilinear threshold model using data of 56 COP26 member countries from 2002 to 2021. Results show that the association between resource endowments and carbon emissions is curvilinear based on two threshold variables: marketization degree and urban services agglomeration. Before they reach certain levels, resource endowments aggravate carbon emissions. After that, resource endowments help carbon reduction. Unlike previous studies that hold resource endowments aggravate emissions, this study finds a condition where resource endowments help sustainable environmental growth. This study also finds that the two threshold variables change with different levels of environmental regulations, R&D expenditures, and enterprise information disclosure. The more favourable these three factors create an easier path to take advantage of resources to reduce carbon emissions. Accordingly, this study recommends enhancing marketization degree and urban services agglomeration to cross the threshold value, and formulating related policies based on countries’ conditions with regards to the three moderate factors.

Towards Sustainable AI: Mitigating Carbon Impact Through Compact Models

As AI technology continues to advance rapidly, it is essential to address the environmental concerns associated with the increasing carbon emissions and their contribution to global warming. The expanding AI industry requires significant computing power, making it a potential major contributor to carbon emissions in the future. Unfortunately, our current understanding of AI models is very limited. We conducted a comprehensive analysis involving 12 distinct AI models, encompassing object detection, translation, and text-to-image generation tasks. Our findings revealed that smaller AI models can achieve equal or even better results compared to larger models while offering a significant reduction of carbon emissions. This highlights the potential for environmental savings by prioritizing smaller models. These findings underscore the importance of considering the environmental impact of AI models and encourage the adoption of strategies such as using smaller models and optimizing workload schedules to reduce carbon emissions. By prioritizing sustainability in AI development and deployment, we can work towards a greener and more sustainable future.

Relationship between the evaluation of agricultural scientific and technological innovation capacity and the influencing factors of green agriculture.

China has limited arable land area, and its output value is increased with enhanced agricultural inputs such as machinery, irrigation, fertilizers, and pesticides. However, this mode is accompanied by an increase in agricultural carbon emissions. With the aim to further examine the driving effect of scientific and technological innovation on economic growth and green agriculture, this study uses the Solow growth model coupled with the Cobb-Douglas production function and vector autoregressive models. Then, the agricultural scientific and technological innovation capacity in Guangdong Province during 2006-2020 is evaluated by using the contribution rate of agricultural scientific and technological progress (ASTP) as the assessment index. In addition, the carbon footprints of green agricultural indexes such as machinery, irrigation, fertilizers, and pesticides are measured to analyze the relationship between green agriculture and agricultural scientific and technological innovation capacity. Results demonstrate the gradual increase in the contribution rate of ASTP in Guangdong Province. During the 11th, 12th, and 13th Five-Year Plan periods, the rates were 65.09%, 65.94%, and 70.40%, respectively, indicating that the agricultural scientific and technological innovation ability constantly improved. Among the indexes of green agriculture, the carbon footprints of machinery have a significant impact on agricultural scientific and technological innovation, which is quickly transformed into machinery. Such innovation requires the driving force of science and technology itself, which have relatively significant and rapid effects. On the basis of the results, corresponding policy suggestions are proposed: increasing investments in scientific and technological innovation in the agricultural field, vigorously developing new energy-saving and emission reduction products and processes for fertilizers, and increasing the research and promotion of agricultural machinery. The proposed method provides good prospects for the development of agricultural production towards mechanization, intelligence, efficiency, and greenness.

Can inter-provincial transmission reduce regional carbon emissions? Evidence from China

Plagued by worsening pollution in load centers along the eastern coastal regions such as Beijing-Tianjin -Hebei, China is reducing local coal-fired power plants in those regions and relying more on inter-regional power transmission. In recent years, China has accelerated the construction of the West-East Electricity Transmission Project and planned and constructed a number of Ultra-high Voltage (UHV) transmission lines. However, how the impact of inter-regional transmission on carbon emissions needs empirical verification. This paper aims to study the carbon emission reduction effect of inter-regional transmission under nested models of the spatial econometric model for panel data. The empirical results show that although the non-dynamic spatial econometric analysis can find the carbon emission reduction effect of inter-provincial transmission, the long-term effect obtained by the dynamic spatial econometric analysis indicates that the inter-provincial transmission can bring about a slight increase in carbon emissions. The inter-provincial power transmission needs to be supported by sustainable share growth of renewable energy to achieve the targeted mitigation of carbon emissions.

Instability in a carbon pool driven by multiple dissolved organic matter sources in a eutrophic lake basin: Potential factors for increased greenhouse gas emissions

Lakes serve as vital reservoirs of dissolved organic matter (DOM) and play pivotal roles in biogeochemical carbon cycles. However, the sources and compositions of DOM in freshwater lakes and their potential effects on lake sediment carbon pools remain unclear. In this study, seven inflowing rivers in the Lake Taihu basin were selected to explore the potential effects of multi-source DOM inputs on the stability of the lake sediment carbon pool. The results showed the high concentrations of dissolved organic carbon in the Lake Taihu basin, accompanied by a high complexity level. Lignins constituted the majority of DOM compounds, surpassing 40% of the total, while the organic carbon content was predominantly composed of humic acids (1.02–3.01 g kg−1). The high amounts of lignin oxidative cleavage led to CHO being the main molecular structure in the DOM of the seven rivers. The carbon constituents within the sediment carbon reservoir exhibited a positive correlation with dissolved CH4 and CO2, with a notable emphasis on humic acid and dissolved CH4 (R2 = 0.86). The elevated concentration of DOM, coupled with its intricate composition, contributed to the increases in dissolved greenhouse gases (GHGs). Experiments showed that the mixing of multi-source DOM can accelerate the organic carbon mineralization processes. The unit carbon emission efficiency was highest in the mixed group, reaching reached 160.9 μmol∙Cg−1, which also exhibited a significantly different carbon pool. The mixed decomposition of DOM from different sources influenced the roles of the lake carbon pool as source and sink, indicating that the multi-source DOM of this lake basin was a potential driving factor for increased carbon emissions. These findings have improved our understanding of the sources and compositions of DOM in lake basins and revealed their impacts on carbon emissions, thereby providing a theoretical basis for improving assessments of lake carbon emissions.

Digital economy, structural deviation, and regional carbon emissions

The comprehensive cohesion of the digital economy, structural deviation adjustment, and green development is a crucial step towards promoting high-quality economic growth and realizing carbon peak and carbon neutrality. This paper probes the impact of intra- and inter-regional digital economy on carbon emissions, considering the mediating effect of structural deviation, utilizing panel data from 267 prefecture-level cities in China spanning the years 2011–2019. The findings reveal the following key insights. First, the connection between the digital economy and carbon emissions within cities follows an inverted “U" shape. In the early stages, the digital economy leads to an average increase in carbon emissions of 0.069%. However, in the later stages, the digitized economy markedly decrease emissions. Second, both the digital economy and carbon emissions exhibit spatial spillover effects. Within the region, DE has a significant nonlinear spillover effect on CO2 emissions in the neighboring regions, with the spatial decay boundary of this effect extending up to 380 km. Third, analysis of the impact mechanism reveals that the digital economy mitigates local and neighboring carbon dioxide emissions by adjusting structural deviation. Heterogeneity analysis indicates that the digital economy is more likely to have a carbon emission reduction effect in cities that are not involved in the industrial transfer and those that do not belong to the old industrial base. These findings provide a rationale and policy reference for China to harness the opportunities presented by digital economy development and to achieve the “dual-carbon” goal.

Social psychological factors drive farmers’ adoption of environmental best management practices

Agriculture is a main driver of land-cover change globally resulting in decreased biodiversity, increased carbon emissions, and land desertification. Environmental best management practices reduce risk to the environment caused by agricultural operations. However, the adoption of environmental best management practices by farmers often is lower than what would be possible. While prior studies have investigated determinants of environmental best management practices, it is not well understood how social psychological drivers of various best management practices may be modified by contextual factors. To help close this knowledge gap, we conducted a large-scale survey of Ontario farmers investigating how social psychological factors and resource constraints combine to determine adoption of farm forests, riparian buffers and windbreaks, and how these relationships are modified by farmer demographics and farm characteristics. We analyzed survey responses of 490 farmers with structural equation modeling using the Theory of Planned Behavior as theoretical framework. Our results suggest that perceived benefits of environmental best management practices are of relatively low impact on best management practice adoption. Beliefs of a personal obligation for adoption and the perception of the capacity for adoption consistently were of higher impact, with lack of labor as main constraint. The relationships of perceived benefits and social norms with adoption were modified by farmer income, education, and age, as well as by the distance between a farm and the nearest settlement. However, the relationship of control beliefs with adoption was not affected by any contextual factors. We conclude that the improvement of programs in support of labor availability may have positive impacts on the adoption of the investigated environmental best management practices.

Spatiotemporal patterns of urban forest carbon sequestration capacity: Implications for urban CO2 emission mitigation during China's rapid urbanization

Urban forests provide ecological functions and human well-being. However, spatiotemporal changes in urban forest carbon sequestration (CS) under rapid urbanization remain poorly understood. We established a model to predict the annual CS dynamics in urban forests based on plot-measured CS and Landsat images. Our results showed that the urban forest coverage in Changchun increased from 18.09 % to 24.01 % between 2000 and 2019, especially in the urban suburbs. However, urban forest patches became more fragmented and less connected, particularly in the urban center. The NDVI is better than other vegetation indices for mapping urban forest CS. We observed a gradual increase in urban forest CS capacity from 2000 to 2019, with higher CS capacity found in urban suburbs compared to urban centers. The class distribution of urban forest CS capacity was skewed toward low values (0–2 g·m−2·d−1), but this tendency diminished gradually. In 2000, the urban forest in Changchun offset approximately 2.11 % of carbon emissions but declined to 0.88 % by 2019 due to increased carbon emissions. Rapid urbanization was the main factor affecting CS, with impervious surface area accounting for 48.7 % of the variation. Urban landscape pattern indices also influenced the CS, with higher forest patch connectivity and lower patch density leading to greater CS capacity. Our study helps urban managers develop urban greening strategies for carbon neutrality and low-carbon city.

Optimal scheduling of hydrogen blended integrated electricity–gas system considering gas linepack via a sequential second-order cone programming methodology

In the context of increasing global environmental pollution and constant increase of carbon emission, hydrogen production from surplus renewable energy and hydrogen transportation using existing natural gas pipelines are effective means to mitigate renewable energy fluctuation, build a decarbonized gas network, and achieve the goal of “carbon peak and carbon neutral” in China. Existing gas network modeling with hydrogen blended is divided into steady-state and dynamic. The steady-state modeling oversimplifies the physical process, while the dynamic modeling is computationally intensive. Thus, a quasi-steady-state modeling method of a hydrogen blended integrated electricity–gas system (HBIEGS) considering gas linepack is developed in this paper. The gas linepack refers to the gas stored in natural gas pipelines due to the compressibility of the gas. As a form of gas energy storage, linepack can enhance system flexibility by coping with wind power uncertainty. In the face of nonlinear gas flow equations in HBIEGS, conventional relaxation or approximate methods cannot provide a sufficiently feasible optimal solution. Therefore, a sequential second-order cone programming (S-SOCP) method is proposed to solve the developed model. It can effectively strike a balance between solution speed and the quality of the solution obtained. Finally, the modified IEEE-24-node power system and 12-node natural gas system are used to validate the effectiveness of the developed model and the proposed method. The optimization results show that the proposed method improves the computational efficiency by 91% compared with a general nonlinear solver.

Digital infrastructure construction and household energy efficiency: Based on a quasi-natural experiment in China

With respect to total emissions, the proportion of household carbon emissions exhibits an upward trend; therefore, to reduce carbon emissions and enhance energy efficiency, researchers should understand the reasons for the increase in household carbon emissions. This study, which is based on the construction of the “Broadband China” demonstration cities and the 2010–2018 China Family Panel Studies, applies a difference-in-differences method. It identifies the impact of digital infrastructure construction on indirect household energy consumption, carbon emissions, and energy efficiency. The estimation results indicate that digital infrastructure construction significantly enhances household energy consumption and carbon emissions, and that it enhances the household energy efficiency pertaining to household carbon emissions and usage. Furthermore, the results indicate that households mainly enhance energy usage and efficiency by increasing developmental consumption. Additionally, this study noted that digital infrastructure construction affects household energy consumption and efficiency through the development of the consumer Internet and industrial Internet, which reshapes household consumption behavior and promotes the producers' technological progress.

An Integrated Analysis on the Synergistic Reduction of Carbon and Pollution Emissions from China’s Iron and Steel Industry

Decarbonization and decontamination of the iron and steel industry (ISI), which contributes up to 15% to anthropogenic CO2 emissions (or carbon emissions) and significant proportions of air and water pollutant emissions in China, are challenged by the huge demand for steel. Carbon and pollutants often share common emission sources, indicating that emission reduction could be achieved synergistically. Here, we explored the inherent potential of measures to adjust feedstock composition and technological structure and to control the size of the ISI to achieve carbon emission reduction (CER) and pollution emission reduction (PER). We investigated five typical pollutants in this study, namely, petroleum hydrocarbon pollutants and chemical oxygen demand in wastewater, particulate matter, SO2, and NOx in off gases, and examined synergies between CER and PER by employing cross elasticity for the period between 2022 and 2035. The results suggest that a reduction of 8.7%–11.7% in carbon emissions and 20%–31% in pollution emissions (except for particulate matter emissions) could be achieved by 2025 under a high steel scrap ratio (SSR) scenario. Here, the SSR and electric arc furnace (EAF) ratio serve critical roles in enhancing synergies between CER and PER (which vary with the type of pollutant). However, subject to a limited volume of steel scrap, a focused increase in the EAF ratio with neglection of the available supply of steel scrap to EAF facilities would lead to an increase carbon and pollution emissions. Although CER can be achieved through SSR and EAF ratio optimization, only when the crude steel production growth rate remains below 2.2% can these optimization measures maintain the emissions in 2030 at a similar level to that in 2021. Therefore, the synergistic effects between PER and CER should be considered when formulating a development route for the ISI in the future.

Land use spatial optimization for city clusters under changing climate and socioeconomic conditions: A perspective on the land-water-energy-carbon nexus

Unbalanced land use development has resulted in water scarcity, high energy consumption, and a significant increase in carbon emissions. Complex and changing environments make it more difficult to manage land use sustainably. This study constructed a variety of future change scenarios coupling climate and socioeconomic development and developed a multidimensional land use spatial optimization (LUSO) model linked with water-energy-carbon. The model is solved using a nondominated sorting genetic algorithm-III (NSGA-Ⅲ) coupled with the information feedback model (IFM). The advantages of this framework include: (1) LUSO based on the clarification of the interactions between different land use types and water-energy-carbon; (2) trade-offs between dimensions are considered to achieve coordinated multidimensional development of the economy, resources, environment, and spatial conversion; and (3) a spatial optimization pattern of land use in response to climate change and socioeconomic development changes can be obtained. The model framework is applied to the Mid-Yangtze River City Cluster for empirical analysis. The results show that socioeconomic development can lead to rapid changes in land use patterns, especially in cultivated and construction land. By 2030, the optimized land use pattern under the intermediate route model is the most suitable land use scenario for the region to achieve sustainable development. Under this scenario, the area of construction land increased by 22.09%, the area of cultivated land decreased by 2.2%, the economic output increased by 76,678.31 (1012 yuan), and the carbon emissions increased by only 5677.79 (107 kg), with an overall sustainability level of 0.85. This study can provide decision-makers with sustainable land resource management options that can respond to changing environments and achieve multidimensional synergistic development.

Education and Energy Consumption: a Provincial Analysis in Indonesia

The global and national energy consumption continues to increase every year, leading to increased carbon emissions. Households are one of Indonesia's energy consumers, where household members' educational level is considered to influence energy consumption. Due to the limited studies in Indonesia regarding the relationship between education and energy consumption, this study aims to explain the relationship between education and energy consumption among provinces in Indonesia. This study is a quantitative study under the STIRPAT framework, using three estimation methods to examine the impact of education on energy consumption among provinces in Indonesia from 2010 to 2021, namely OLS, Fixed Effect, and Fixed Effect Discroll-Kraay. The research was conducted with three different data analyses: Indonesia as a whole, Java, and non-Java regions. The study found differences in estimation results between Java and non-Java regions, providing evidence that the level of education has different associations with per capita energy consumption in these two areas. It was also found that the estimation results for Indonesia align with those in non-Java regions. The study concludes that education has a significant positive association with energy consumption in Indonesia and specifically in non-Java regions, while the association between education and energy consumption in Java is negative and significant. The policy implications suggest that the government should continue efforts to improve the quality of education and promote more efficient energy consumption education in the Java region and implement complementary policies such as compulsory education programs for non-Java regions to reduce energy consumption in Indonesia.

Changes in the ecosystem service importance of the seven major river basins in China during the implementation of the Millennium development goals (2000–2015) and sustainable development goals (2015–2020)

In 2015, the Sustainable Development Goals (SDG) replaced the Millennium Development Goals (MDG), providing guidance and direction for the future development of countries around the world. Both MDG and SDG have established multiple indicators to achieve environmental sustainability. However, few studies have compared the changes in the ecological environment during the two periods. Therefore, this study selected the seven major river basins in China as the study area and comprehensively assessed the changes in ecosystem service importance (ESI) within different basins in the MDG and SDG periods based on six ecosystem services, including habitat quality, soil conservation, water yield, carbon storage, grain production, and aesthetic landscape. Specifically, the changes from 2000 to 2015 and 2015 to 2020 were used to represent MDG and SDG periods, respectively. The results showed that the overall ESI of the seven major river basins showed an increasing trend in both periods, and the proportion of extremely important basins reached 20.39% in 2020, mostly distributed in the Yangtze River basin and the Pearl River basin. Except for carbon storage, other ecosystem services were restored in the SDG period. However, COVID-19 and a series of sudden disasters in 2020 have greatly affected grain production and the aesthetic landscape. The fluctuation of habitat quality also indicated that although the ecological environment has a trend of recovery, there was still some degradation compared to 20 years ago. National protection policies and restoration projects have gradually improved the ecological environment of the seven river basins and are striving to reach the SDG standards step by step. However, many problems still need to be solved urgently, such as the reduction of ecological land, the increase in carbon emissions, and the weak resilience to disasters. China has made significant positive progress in MDG, and more efforts are needed to achieve the SDG goals in the future.

Bifurcation Points of Carbon Regulation

There are 1800 climate change laws around the world. In recent years, the rapid increase in carbon emissions has caused global warming and climate pollution, causing serious harm to social development and human health. Reducing carbon emissions is getting a lot of attention. Since the Kyoto Protocol and the Paris Agreement, many countries have made efforts to reduce carbon emissions.The article describes the international processes intended to adopt regulations on greenhouse gas emissions, including the regulated market for quotas and the voluntary market for reducing greenhouse gas emissions. The emphasis is on European and Russian quota mechanisms. The stages of development of transborder carbon regulation in the EU are highlighted. It is noted that in Russia, the leader in application of ESG criteria is JSC Russian Railways, as a company that is consistently pursuing the transition to implementing the principles of sustainable development.The conclusions on international trends in development of carbon regulation are followed by proposals on solutions regarding problematic aspects of the new Russian carbon legislation. According to the authors, the target scenario remains the transition to a new technological structure ensuring a real reducing the carbon footprint.

Life-cycle-based multi-objective optimal design and analysis of distributed multi-energy systems for data centers

The widespread deployment of data centers has contributed to a dramatic increase in energy consumption and carbon emissions. To solve this problem, this study proposes a distributed multi-energy system (DMES) driven by solar, wind, geothermal and natural gas and its life-cycle-based multi-objective optimization model considered energy, economy, and environment. Moreover, a novel operation strategy based on load characteristics is presented for energy flow allocation and a multi-objective grasshopper optimization algorithm is improved for model solving. Case studies are conducted on a practical data center in Qinghai, China. Based on the DMES capacity configuration and scheduling results, the cooling, heating and power supply can satisfy the demand of the data center while generating over 30% of the power from renewable sources. In addition, carbon tax is considered in the optimization model, which accounts for 4% of the life-cycle cost. Furthermore, the sensitivity of carbon tax, equipment lifespan and predicted mean vote is analyzed, obtaining that these factors have some influence on the performance of the system. Finally, the uncertainty impact on system planning is studied, concluding that the designed system exhibits high robustness levels when renewable energy forecast error is less than 20% or load forecast error is less than 10%.

How does the digital service trade nonlinearly affect carbon emissions? Empirical evidence from G20 countries.

This study explores the nonlinear relationship between digital service trade and carbon emissions using the data of 42 G20 countries from 2005 to 2021. It is concluded that this relationship appears to be an inverted U-shape. When the digital service trade is inadequate, this exacerbates carbon emissions. Only when the digital service trade is above this point does it become beneficial to environmentally sustainable development. Mediation analyses indicate that the digital service trade increases carbon emissions by increasing emissions from digital infrastructure and decreases them by optimizing the industrial structure and adopting low-carbon technologies. Furthermore, the nonlinear association is moderated by human capital. When human capital is insufficient, it is harder for the digital service trade to curb carbon emissions. Accordingly, this study is helpful to develop the digital service trade and achieving the goals of carbon peak and neutralization and the sustainable growth of the environment.

Spatiotemporal differences in and influencing effects of per-capita carbon emissions in China based on population-related factors

Intensive human activities and resource consumption in China have led to increasing carbon emissions, placing enormous pressure on achieving sustainable development goals. Nonetheless, the effects of population-related factors and carbon emissions remain controversial. This study focuses on the spatiotemporal differences in and influencing effects of per-capita carbon emissions using 2010–2019 panel data covering 30 regions in China. Differing from previous studies, population-related factors are employed to classify the 30 regions into 4 classes, and kernel density estimation, σ convergence and spatial econometric models are used to analyse the spatiotemporal differences in and influencing effects of per-capita carbon emissions. The results demonstrate that overall per-capita carbon emissions rose, but there was heterogeneity in the change in per-capita carbon emissions in the 4 classes of regions. The difference in regional per-capita carbon emissions has been widening, but the change rate of the difference stabilized. Overall, per-capita carbon emissions are heavily affected by household size; however, the driving forces behind per-capita carbon emissions in the 4 classes of regions vary. These results suggest that precise and coordinated governance of carbon emissions and reverting to the traditional household structure should be considered to meet the dual carbon goal.