Factors Affecting CO2 Emissions Research Articles

Quantifying the trends and affecting factors of CO2 emissions under different urban development patterns: An econometric study on the Yangtze river economic belt in China

Environmental issues are rarely investigated within the framework of different urban development patterns, especially concerning CO2 emissions. This research works to reveal the trends and determining factors of urban CO2 emissions under different urban development patterns. Taking the cities in the Yangtze River Economic Belt (YREB) as an example, this study develops an identification system in light of the dynamics of urban population and population density to categorize urban development patterns as growth, potential shrinkage, smart shrinkage, and continuous shrinkage. Then, the Theil-sen medium analysis and spatial econometric modeling are applied to shed light on the trend and influencing factors of CO2 emissions, respectively. The findings indicate that urban growth and potential shrinkage are the main development patterns in the YREB, and the percentage of growing cities and potentially shrinking cities is more than 77 % across the different geographic areas of the YREB. CO2 emissions are remarkably heterogeneous under different urban development patterns. Over 90 % of cities show a growth trend or fluctuation in per capita CO2 emissions under the four development patterns during 2010∼2019. Only a few growing and potentially shrinking cities show a decreasing trend in per capita carbon emissions, suggesting a severe peak carbon challenge in the YREB. CO2 emissions in growing and shrinking cities are significantly influenced by their neighboring cities. Factors affecting CO2 emissions are different in shrinking and growing cities, and spatial spillover effects enhance the role of these factors. Compared with growing cities, improving population concentration, developing appropriate tertiary industries, strictly controlling land expansion, and developing green areas can pave the way for carbon emission reduction in shrinking cities. This study can support designing efficient carbon emission reduction measures for various cities.

Building a greener environment: education levels and their links to CO2 reduction

ABSTRACT Exploring the factors affecting CO2 emissions is important for governments and policymakers to control the increasing trend of CO2 emissions. Although many factors have been studied by researchers, education as a potential factor did not attract their attention. Thus, this study focuses on education levels as a new perspective to examine how education levels can contribute to CO2 emission reductions in the USA. Autoregressive Distributed Lag Model (ARDL) was conducted, using relevant factors, such as: Gross Domestic Product (GDP), total primary energy consumption (EC), elementary school enrolment ratio (EER), high school enrolment ratio (HER), and college enrolment ratio (CER). The results reveal that GDP and EC have positive and significant effects on CO2 emissions in the short- and long-run. The effects of the three education levels are significant and negative in the long-run, while in the short-run only the coefficient of CER is significant. This study confirms a significant link between education levels and CO2 emissions reduction in the long-run, and hopes to provide insight for governments and policymakers that investing on education levels, prioritizes the environmental awareness and leads to a more environmentally conscious society.

Modeling and forecasting CO2 emissions in China and its regions using a novel ARIMA-LSTM model

Since China joined the WTO, its economy has experienced rapidly growth, resulting in significantly increase in fossil fuel consumption and corresponding rise in CO2 emissions. Currently, China is the world's largest emitter of CO2, the regional distribution is also extremely uneven. so, it is important to identify the factors influence CO2 emissions in the three regions and predict future trends based on these factors. This paper proposes 14 carbon emission factors and uses the random forest feature ranking algorithm to rank the importance of these factors in three regions. The main factors affecting CO2 emissions in each region are identified. Additionally, an ARIMA + LSTM carbon emission predict model based on the inverse error combination method is developed to address the linear and nonlinear relationships of carbon emission data. The findings suggest that the ARIMA + LSTM is more accurate in predicting the trend of CO2 emissions in China. Moreover, the ARIMA + LSTM is employed to forecast the future CO2 emission trends in China's east, central, and west regions, which can serve as a foundation for China's CO2 emission reduction initiatives.

Assessing the possibility of China reaching carbon emission peak by 2030 in the context of the COVID-19 pandemic.

At this stage, it is an important goal for China to solve environmental problems to limit the carbon emission peak by 2030 and then to lower its quality gradually thereafter. The outbreak of the COVID-19 pandemic in 2020 seriously impacted all aspects of China's social economy and brought many uncertainties to the realization of the carbon emission peak. Based on the fact, it has important theoretical and practical significance to take the problem of China's carbon emissions during the COVID-19 as the research object. Thus, this paper analyzes the current situation of China's CO2 emissions and finds out that in the context of ongoing COVID-19 response, stimulated by China's economic stimulus policies, CO2 emissions decrease firstly and then gradually rebound. On this basis, the paper constructs a dynamic model of China's CO2 emission system to simulate the change in China's CO2 emissions under different economic stimulus policies. The relevant simulation results demonstrate the following: (1) China cannot realize the CO2 emission goal by 2030 only by adopting traditional economic stimulus policies. (2) The green economic stimulus policy oriented to the development of clean energy will rebound China's CO2 emissions in the short term, but it can effectively reduce CO2 emissions in the long run. The most important factors affecting CO2 emission reduction are industrial energy consumption and industrial structure, while the energy power generation structure and the transportation structure have a relatively weak impact on the emission reduction effect. (3) The green economic stimulus policy combined with economic stimulus measures and a variety of low-carbon measures can enable China to peak CO2 emissions before 2030, with a peak value of 11.059 billion tons. In general, green economic stimulus policies can achieve a win-win situation for China's economic recovery and carbon emission peak.

Life Cycle Carbon Dioxide Emissions and Sensitivity Analysis of Elevators

With the intensification of climate warming, the carbon dioxide emissions from high-energy-consuming elevators have attracted increasing societal attention. The assessment of carbon dioxide emissions, particularly the boundaries and strategies of carbon dioxide emissions accounting, lacks systematic research. However, an efficient evaluation of elevator carbon dioxide emissions is beneficial for improving elevator energy utilization. A carbon dioxide emissions accounting method and inventory analysis of a life cycle for an elevator is proposed to measure the carbon dioxide emissions from production to disposal. In addition, a new assessment indicator, namely, annual carbon dioxide emissions per ton·kilometer, is proposed to evaluate the carbon dioxide emissions for different types of elevators. The lifetime carbon dioxide emissions of the elevator and its sensitivity to influencing factors were assessed. The results indicate that the carbon dioxide emissions in the four stages of manufacturing, installation, operation and maintenance, and demolition and scraping contributed 41.31%, 0.92%, 57.32% and 0.44%, respectively. The annual carbon dioxide emissions of the elevator were about 27.18 kgCO2/t·km. The four primary factors affecting CO2 emissions were electricity consumption, printed circuit boards, low-alloy steel and chrome steel in descending order. Their probability distribution characteristics all obeyed triangular or uniform distributions. The median of their 95% confidence intervals was about 73,800. Their coefficients of variation were all below 2.1%. The effective strategies for energy conservation and carbon reduction were suggested by the life cycle impactor assessment. They also provide guidance for sustainable elevators.

What is the role of remittance and education for environmental pollution? - Analyzing in the presence of financial inclusion and natural resource extraction

This study assessed the impact of gross domestic product (GDP), education, natural resources, remittances, and financial inclusion on carbon emissions in G-11 countries from 1990 to 2021. Based on the negative impact of pollution and the need for sustainable development, this study examined factors affecting CO2 emissions in G-11 countries using non-linear panel ARDL model. The study found that a positive GDP shock increases CO2 emissions in the short and long term, while a negative shock decreases emissions in the short term and increases emissions in the long term. Education was found to increase CO2 emissions in the long term but decrease them in the short term, emphasizing the need for education on combating emissions. Natural resources were also found to increase emissions in the long term, highlighting the need for government-defined institutions to minimize extraction effects and enforce transparency and accountability. Positive changes in personal remittances and financial inclusion were found to increase emissions in both the short and long term, suggesting the need for policies that encourage renewable energy sources and energy efficiency improvement. The study concludes that policymakers should prioritize efficient resource allocation, promote renewable energy usage, and enhance environmental awareness to achieve sustainable development goals in G-11 countries. The possible applications of this study include the use of the models to investigate the asymmetric effects on CO2 emissions. This model can be applied in future studies to examine the relationship between GDP, education, natural resources, personal remittances, financial inclusion, and CO2 emissions in other countries.

Social, economic, and technical factors affecting CO2 emissions in Iran.

Most scholars support the increase in carbon dioxide (CO2) emissions as one of the major causes of the increase in global climate change. Therefore, reducing CO2 emissions from the main emitter countries, including Iran as the sixth emitter, is important to deal with the harmful effects of global climate change. Accordingly, the main aim of this paper was to analyze the social, economic, and technical factors affecting CO2 emissions in Iran. Previous studies on diverse variables affecting emissions are not very accurate and reliable as they do not consider indirect effects. This study applied a structural equation model (SEM) to estimate the direct and indirect impacts of factors on the emissions by panel data for 28 provinces of Iran from 2003 to 2019. According to geographical location, three distinct regions, the north, center, and south of Iran were considered. The findings suggest that a 1% increase in social factor directly increased CO2 emissions by 2.23% (in the north) and 1.58% (in the center), but indirectly reduced emissions by 0.41% (in the north) and 0.92% (in the center). Hence, the total effects of the social factor on CO2 emissions were estimated at 1.82%, and 0.66% in the northern, and central regions, respectively. In addition, the total effects of the economic factor on CO2 emissions were estimated at 1.52%, and 0.73% in those regions. The results of this study showed that the direct effects of a technical factor on CO2 emissions were negative in the north and center. However, they were positive in the south of Iran. Based on the empirical results of this study, three policy implications are discussed in order to control CO2 emissions in regional distinctions of Iran as follows: First, policymakers should pay attention to the social factor, i.e., the growth of human capital in the southern region with the aim of increasing sustainable development. Second, Iranian policymakers must prevent unilaterally increasing gross domestic product (GDP) and financial development in the north and center. Third, policymakers should pay attention to the technical factor, i.e., improving energy efficiency, as well as upgrading information and communications technology (ICT) in the northern and central regions, and limiting the technical factor in the southern region.

Multi-process production occurs in the iron and steel industry, supporting ‘dual carbon’ target: An in-depth study of CO2 emissions from different processes

Reducing CO2 emissions of the iron and steel industry, a typical heavy CO2-emitting sector, is the only way that must be passed to achieve the ‘dual-carbon’ goal, especially in China. In previous studies, however, it is still unknown what is the difference between blast furnace-basic oxygen furnace (BF-BOF), scrap-electric furnace (scrap-EF) and hydrogen metallurgy process. The quantitative research on the key factors affecting CO2 emissions is insufficient. There is also a lack of research on the prediction of CO2 emissions by adjusting industrial structure. Based on material flow analysis, this study establishes carbon flow diagrams of three processes, and then analyze the key factors affecting CO2 emissions. CO2 emissions of the iron and steel industry in the future is predicted by adjusting industrial structure. The results show that: (1) The CO2 emissions of BF-BOF, scrap-EF and hydrogen metallurgy process in a site are 1417.26, 542.93 and 1166.52 kg, respectively. (2) By increasing pellet ratio in blast furnace, scrap ratio in electric furnace, etc., can effectively reduce CO2 emissions. (3) Reducing the crude steel output is the most effective CO2 reduction measure. There is still 5.15 × 108-6.17 × 108 tons of CO2 that needs to be reduced by additional measures.

Do green innovation, I.C.T., and economic complexity matter for sustainable development of B.R.I. economies: moderating role of higher education.

The research intends to enlarge the environmental economics literature by displaying the probable mechanisms between green innovation, higher education, and sustainable development. In the context of a new era, sustainability faces challenging obstacles. Many studies have looked at fundamental factors affecting CO2 emissions, while the impact of green innovation and higher education is essential but mostly ignored. This study looked at 60 Belt and Road Initiative (B.R.I.) economies to see how factors, including green innovation, economic complexity index, I.C.T., and higher education, affect carbon emissions in the presence of sustainable development using annual data from 2000-2020. In order to calculate the persistence of the connection between the factors, this research uses the CS-ARDL. The results' robustness and reliability were examined using PMG estimation. The results indicate that the economic complexity index and urbanization positively impact carbon emission (CO2). Higher education (E.D.U.) has a significant positive impact in the short run and a negative effect in the long run-on carbon emissions. Similarly, information and communication technology (I.C.T.) and green innovation have a negative impact on carbon emission (CO2). Moreover, the results indicate that the moderate effect of green innovation with economic complexity index, information and communication technology, and higher education has a negative impact on carbon emission. The estimated coefficients also provide significant policy implications for the chosen and the other developing markets in designing an adequate route ahead to a sustainable environment.

Emissions of CO2 from downed logs of different species and the surrounding soil in temperate forest

The decomposition of deadwood plays a very important role in the functioning of the forest ecosystem. The present study was conducted with the objectives to: (1) determine the amount of deadwood respiration depending on species and degree of decomposition; (2) determine the extent of the impact of decomposing wood on the amount of respiration in surrounding soil; (3) find a relationship between the amount of respiration and the chemical fractional composition of soil organic matter. Our research has shown that respiration of decaying wood samples was 2-3 times lower compared to soil, regardless of the type of wood and the degree of wood decomposition. The conducted analyses confirmed the influence of the species of wood and the degree of decomposition on the respiration rate in wood samples. More decomposed wood (4th and 5th degree of decomposition) releases more CO2 compared to less decomposed wood and the highest CO2 emissions were recorded for aspen and alder wood. Better understanding of the mechanisms and factors affecting CO2 emissions in forest ecosystem can help reduce climate change.

Predicting the Environmental Change of Carbon Emission Patterns in South Asia: A Deep Learning Approach Using BiLSTM

China’s economy has made significant strides in the past three decades. As a direct result of China’s “one belt, one road” (OBOR) initiative, the country’s rate of industrialization and urbanization is currently the fastest in the entire world. This rapid development is largely dependent on the enormous amounts of energy currently being consumed and forms the foundation of the world’s high levels of carbon emissions. It is generally agreed that the production of greenhouse gases, particularly carbon dioxide, is the primary contributor to the current state of climate change. In this paper, a CO2 emission prediction model based on Bi-LSTM is constructed. In order to conduct empirical tests on the model, this study uses data from South Asian countries and China from 2001 to 2020. China’s CO2 emissions from 2022 to 2030 were predicted along with those of other countries in order to study the combined effects of the scientific and technological progress, industrial structures, and energy structure factors affecting CO2 emissions. When compared with the LSTM and GRU methods, the Bi-LSTM model’s results produced lower MAE, MSE, and MAPE values, indicating that it performs better. According to the findings, carbon emissions represent a significant problem that will become much worse in the future due to China and India’s high emissions, particularly in the next 10 years, if the government does not implement policies that help reduce those emissions.

Analysis of Factors Affecting CO2 Emissions and the Kuznets Curve Environmental Hypothesis: Study on G-20 Countries 2013-2018 Period

This study aims to determine the effect of economic growth, population, industrialization, energy consumption and fossil fuel consumption of CO2 emissions in G-20 countries. Panel data is used as a data analysis technique in this study. The variables used are based on the concept of the Environmental Kuznets Curve. The Kuznets hypothesis explains that an increase in economic growth reduces inequality and poverty in a certain period of time (or referred to as the turning point limit). This study focuses on the G-20 countries in the 2013-2018 period. Based on the regression results of the CO2 emission variables (CO2), gross domestic product (GDP), energy consumption (KE), population (JP), industrialization (IND) and fossil fuel consumption (BBF) it was found that all independent variables simultaneously (GDP, KE, JP, IND and BBF) have a significant effect on the dependent variable (CO2). Furthermore, partially significant variables that affect CO2 emissions are GDP, JP, IND and BBF, while the variables that have no significant effect on CO2 emissions are only KE. Support and commitment to policies both nationally and regionally are needed to reduce environmental degradation through inclusive economic growth in G-20 countries.

Economic complexity, ICT, biomass energy consumption, and environmental degradation: evidence from Iran.

Economic complexity, biomass energy consumption, and information communication technology (ICT) have diverse impacts on energy consumption and carbon dioxide (CO2) emissions. Nevertheless, analysis of these variable effects is not addressed in the previous literature; the antiqueness of this article is stuffing this gap. This study assessed the relationship between gross domestic product (GDP) per capita, biomass consumption, economic complexity index (ECI), ICT, and CO2 emissions in Iran in 1994-2018. The autoregressive distributed lag (ARDL) model and the quantile regression (QR) econometric technique were used to investigate the factors affecting CO2 emissions in the tails of the conditional distribution. The share of each influential factor was predicted through the variance decomposition analysis (VD) for the next 10years. The empirical results showed a long-run relationship between the variables. So, the variables of biomass consumption, ECI, and ICT improve the quality of the environment in Iran by reducing CO2 emissions, and the per capita GDP variable increases CO2 emissions. Results suggest no evidence indicating the presence of environmental Kuznets curve (EKC); however, QR demonstrated the existence of EKCs in the lower quantiles of the conditional distribution. The ECI will have the most share to change the CO2 emissions in the future. The income threshold should be determined at the turning point of the EKC to increase economic development. Moreover, investing in increasing biomass consumption is vital. Policymakers also need to consider strict added value for the export of products.

Measuring the Critical Influence Factors for Predicting Carbon Dioxide Emissions of Expanding Megacities by XGBoost

CO2 is the main greenhouse gas. Urban spatial development, land use, and so on may be affected by CO2 and climate change. The main questions studied in this paper are as follows: What are the drivers of CO2 emissions of expanding megacities? How can they be analyzed from different perspectives? Do the results differ for megacities at different stages of development? Based on the XGBoost model, this paper explored the complex factors affecting CO2 emissions by using data of four Chinese megacities, Beijing, Tianjin, Shanghai, and Chongqing, from 2003 to 2017. The main findings are as follows: The XGBoost model has better applicability and accuracy in predicting carbon emissions of expanding megacities, with root mean square error (RMSE) as low as 0.036. Under the synergistic effect of multiple factors, population, land size, and gross domestic product are still the primary driving forces of CO2 emissions. Population density and population become more important in the single-factor analysis. The key drivers of CO2 emissions in megacities at respective developmental stages are different. This paper provides methods and tools for accurately predicting CO2 emissions and measuring the critical drivers. Furthermore, it could provide decision support for megacities to make targeted carbon-emission-reduction strategies based on their own developmental stages.

Renewable energy and economic growth relationship under the oil reserve ownership: Evidence from panel VAR approach

This study aims to explore the relationship among economic growth, CO2 emissions, fossil fuels consumption, renewable energy consumption, foreign direct investments, and trade by taking into account countries with oil reserves (export groups) and countries without oil reserves (import group). The factors affecting CO2 emissions in countries with and without oil production have been investigated by using Panel VAR (PVAR) and Granger causality methods for the period of 1990–2015. In both country groups, growth positively affects CO2 emissions. While there is a negative relationship between CO2 emission and foreign direct investments in the oil importing country group, there is a positive relationship between CO2 and trade in the oil-exporting group. PVAR analysis results illustrate that the growth hypothesis is valid in both groups of countries. In addition, when both country group examined, the countries sacrifice their national income to reduce carbon emissions, which makes it difficult to reduce emissions in the world. According to the variance decomposition results, while the CO2 variable defines itself as 98% for oil importing countries, the second important variable that pollutes the air is GDP. In addition, the variables FDI, REN and FOSSIL affect air pollution by 4% over a 10-year period.

Impact of Economic Affluence on CO2 Emissions in CEE Countries

There is huge evidence for a relationship between economic growth and environmental degradation. One of the causes of environmental degradation is CO2 emission which is added to the atmosphere through human activities and excessive industrialization. The aim of this research is to examine the relationship between CO2 emissions and measures of wealth in countries of Central and Eastern Europe between 2000 and 2019. The paper extends the research on economic affluence by taking into consideration two measures of economic growth, in addition to GDP, the HDI index is included. The basis for the investigation is the EKC concept. All analyses are based on econometric models with GDP and the HDI index as independent variables. The results are not conclusive and there is no one model which best describes the relationship between CO2 emissions and economic growth. Verification of the models indicates the better fit of models with the HDI index as the measure of affluence. Moreover, the study confirms that the key factors affecting CO2 emissions are energy consumption per capita which leads to an increase in CO2 emissions, and renewable energy consumption which reduces CO2 emissions. Therefore, technological changes and an increase in human awareness of global sustainability are required.

Natural resources and CO2 emissions nexus: Sustainable development path for South Asian countries

The rapid deterioration of the environment has attracted the world’s attention to find the determinants of carbon dioxide (CO2) emissions. In this line, many studies have probed the factors affecting CO2 emissions but the role of natural resources has been overlooked significantly. Therefore, this study attempts to fill this gap by investigating the role of natural resources in CO2 emissions in four South Asian countries over the annual period of 1990-2019. Other variables of trade openness, renewable energy, energy use and gross domestic product (GDP) were also included in the model. Findings of the autoregressive-distributed lag (ARDL) method reveal that natural resources reduce CO2 emissions in Pakistan and India. The abundance of natural resources, increases CO2 emissions in Bangladesh. This association was not found significant in Sri Lanka. Moreover, this research confirmed Environmental Kuznets curve (EKC) in Pakistan, India, Bangladesh and Sri Lanka. Renewable energy is reducing CO2 emissions in these countries. Therefore, natural resources are essential to reduce CO2 emissions in Pakistan and India. Bangladesh needs to utilize its natural resources efficiently to improve air quality.

Spatial Heterogeneity of Factors Influencing CO2 Emissions in China’s High-Energy-Intensive Industries

In recent years, China has overtaken the United States as the world’s largest carbon dioxide (CO2) emitter. CO2 emissions from high-energy-intensive industries account for more than three-quarters of the total industrial carbon dioxide emissions. Therefore, it is important to enhance our understanding of the main factors affecting carbon dioxide emissions in high-energy-intensive industries. In this paper, we firstly explore the main factors affecting CO2 emissions in high-energy-intensive industries, including industrial structure, per capita gross domestic product (GDP), population, technological progress and foreign direct investment. To achieve this, we rely on exploratory regression combined with the threshold criteria. Secondly, a geographically weighted regression model is employed to explore local-spatial heterogeneity, capturing the spatial variations of the regression parameters across the Chinese provinces. The results show that the growth of per capita GDP and population increases CO2 emissions; by contrast, the growth of the services sector’s share in China’s gross domestic product could cause a decrease in CO2 emissions. Effects of technological progress on CO2 emissions in high-energy-intensive industries are negative in 2007 and 2013, whereas the coefficient is positive in 2018. Throughout the study period, regression coefficients of foreign direct investment are positive. This paper provides valuable insights into the relationship between driving factors and CO2 emissions, and also gives provides empirical support for local governments to mitigate CO2 emissions.

The Heterogeneity Research of the Impact of EPU on Environmental Pollution: Empirical Evidence Based on 15 Countries

While economic growth has been the main goal of countries around the world, environmental problems such as air pollution have also arisen. Since the increase in economic uncertainty is limiting production capacity and consumers’ marginal propensity to consume, which reduces CO2 emissions, economic policy uncertainty has become one of the most important factors affecting CO2 emissions. COVID-19 has demonstrated that economic policy uncertainty reduces the enthusiasm of market participants, which, in turn, reduces energy demand and CO2 emissions. In order to further study the impact of economic policy uncertainty on air pollution, this study uses a panel model to empirically test the data for a sample of 15 countries covering the period from 1997 to 2019. According to the empirical results, we find that the economic policy uncertainty has a significant negative impact on per capita CO2 emissions. That is, the higher the uncertainty of economic policy, the lower the per capita CO2 emissions of countries. What’s more, this negative effect is larger in emerging market countries than in advanced countries.

Driving forces of carbon dioxide emissions in China’s cities: An empirical analysis based on the geodetector method

Reducing carbon dioxide (CO2) emissions is an important strategy used to curb global warming. Notably, urban areas contribute to the majority of global CO2 emissions. The exploration of spatial differences and their impacts on CO2 emissions in cities serves an essential role in formulating regional carbon emission reduction strategies. Therefore, combining spatial autocorrelation analysis with a geographical detector model could help understanding how CO2 emissions are spatially distributed in cities and the mechanisms responsible for their distribution. By analyzing China’s prefecture-level cities as spatial unit, the spatial distribution features of CO2 emissions were investigated, and their main drivers in 2005 and 2012 were identified. The results indicates that CO2 emissions increased by 60.35% from 2005 to 2012. The higher CO2 emissions in cities were mainly located in the east and north regions of China. CO2 emissions had a significant positive spatial autocorrelation and an obvious phenomenon of stratification appeared in 2005 and 2012. The degree of spatial agglomeration among cities with low and high CO2 emissions both increased. Gross domestic product (GDP), research and development (R&D), foreign direct investment (FDI), and urban built-up area were major factors affecting CO2 emissions. Increased population density was associated with decreased CO2 emissions, while the effect of climate types increased from 2005 to 2012. The interactions of GDP with share_ industry (the share of labour in the secondary industry), population density, and urbanization had a stronger influence on CO2 emissions in 2005, while the interactions of GDP with FDI, climate types, and share_ industry were stronger in 2012. However, the influence of share_ industry and climate types on CO2 emissions were only important when certain economic levels were met. The findings provide a reference for decision-makers to develop more accurate and detailed policies to reduce CO2 emissions in Chinese cities.