Proportion Of Secondary Industry Research Articles

The deviation characteristics and influencing factors of urban scaling laws in China: industrial and sectoral perspective

ABSTRACT When facing the complex and enormous system of cities, it is imperative to interpret urban development phenomena in a scientifically rigorous manner. Urban scaling laws describe the logarithmic relationship between the population size of a city and various systemic indicators, providing insights into the reasons behind urban population distribution patterns and scaling characteristics. While research related to urban scaling laws has garnered increasing attention, current studies often focus on revealing the relationships between urban size and economic, social, and infrastructural indicators, neglecting investigations into the relationships between population size and industrial sector indicators. Therefore, this study approaches the topic from the perspective of industries and sectors, employing methods such as ordinary least squares regression and comparative analysis to identify the deviation characteristics of urban scaling laws in China. Additionally, the XGBoost + Shap value modeling method is utilized to discern the factors influencing these deviations. The study findings indicate that from 1988 to 2019, China’s secondary industry scaling index exhibited superlinear growth, while the tertiary industry scaling index demonstrated linear growth, with significant differences observed across various industries. The range of deviation in China’s industry scaling index is relatively large. Among the variables considered, the growth rate of gross domestic product (GDP) exerts the strongest influence on the industry scaling index, followed by urbanization growth rate and urbanization rate. Factors such as population density, per capita national income, and the proportion of secondary industry to GDP also impact industry scaling indices. The research reveals disparities in urban development and suggests responses through urban policy interventions, thereby providing a basis for urban planning and policy formulation.

Evolution characteristics, carbon emission effects and influencing factors of production-living-ecological space in Taihang Mountain poverty belt, China

The rapid advancement of urbanization and industrialization in China has gradually spread to the poor mountainous areas, which has not only brought about rapid economic development but has also caused the increasing competition for production-living-ecological spaces (PLES) and many ecological and environmental problems, carbon emissions have also increased. As an economically less developed and ecologically fragile area in China, whether the transition of the PLES in the mountain poverty belt has unique characteristics? How the PLES transition in mountainous areas affects carbon emissions and what are the important factors affecting carbon emissions? To explore these issues in depth, we studied the Taihang Mountain area in Shijiazhuang (TMS) using remote sensing image interpretation data from 2000, 2010, and 2020, and we analyzed the PLES evolution characteristics, carbon emission changes, carbon emission effects and its influencing factors of PLES. The results are as follows: 1) The TMS was dominated by ecological and production space. From 2000 to 2020, the production space decreased by 384.66 km2, the ecological space increased by 123.80 km2, and the living space increased by 260.86 km2. Agricultural production space was mainly converted to ecological and rural living space. Industrial and mining productive space was mainly converted to agricultural productive space and urban living space. 2) The study area was in a state of carbon deficit, the transition of ecological space and agricultural productive space to industrial and mining productive space and living space were the main transition types caused the carbon emissions increasing, and that of industrial and mining productive space to agricultural productive space was the main type caused the carbon emissions decreasing. 3) The proportion of construction land, urbanization rate and proportion of secondary industry are the main factors leading to the increase of carbon emissions. Per capita energy consumption, forest coverage and proportion of tertiary industry are the main factors leading to the decrease of carbon emissions. This can provide new ideas for research on carbon emissions from land-use changes and a theoretical basis for the optimization of territorial space in the mountainous areas of China.

Diversified evolutionary patterns of surface urban heat island in new expansion areas of 31 Chinese cities

As a hot area of population inflow in the process of urbanization, the urban expansion area faces rapid growth of surface urban heat island (SUHI). However, the multi-dimensional evolutionary characteristics of SUHI in urban expansion areas are still unclear. Through analyzing the evolution of SUHI range ratio, mean intensity and maximum intensity, in this study we identified the comprehensive evolutionary pattern of summer SUHI in urban expansion areas of 31 major cities in China during 2000–2018, and further investigated the corresponding dominant influencing factors. The results showed that the SUHI range and intensity in urban expansion areas exhibited a significant increasing trend in 81% and 71% of the cities respectively during the summer daytime. The cities with declining SUHI range and intensity were distributed in northwestern high-altitude areas with low economic level, while the cities with dominant increases in the SUHI range were distributed in eastern low-altitude areas with high economic level. Climate conditions and population distribution were the dominant influencing factors of intensity-dominated increasing and balanced increasing types, respectively. The intensity and range two-dimensional increasing type had experienced the most severe SUHI growth, with large proportion of secondary industry as the main influencing factor. This study highlighted the importance of multi-dimensional characteristics of SUHI evolution, which provided a new insight to understand SUHI change in urban expansion areas and associated mitigation measures.

The synergy between pollution reduction and carbon reduction in Chinese cities and its influencing factors

The synergy between pollution reduction (PR) and carbon reduction (CR) is crucial for cities facing both climate change and environmental pollution in achieving sustainable development. This study evaluates the PR-CR synergy in 295 Chinese cities from 2014 to 2022. Utilizing the improved Tapio Decoupling Principle and the Probit model, the study reveals that: (1) About 51.5 % (152) of the cities demonstrated a synergy between PR and CR. This synergy varied across the years; for instance, 102 cities exhibited it in 2018 and 2020, but only 14 in 2019. (2) The remaining 48.5 % (143 cities) did not exhibit this synergy, largely due to increased carbon emissions. These cities fall into three categories: those affected primarily by air pollution, carbon emissions, or both. (3) Environmental regulation emerged as the most influential factor promoting this synergy, with a significant coefficient of 81.47. Other positive influences included annual precipitation (0.000284), altitude (0.000650), Normalized Difference Vegetation Index (2.024), proportion of secondary industry (0.0168), proportion of construction land area (0.0182), and green patents (0.0000571). Conversely, average annual temperature (-0.0620) and relief amplitude (-0.0705) negatively impacted the synergy. This study enriches the discourse on PR-CR synergy, offering valuable insights for shaping sustainable development policies in cities.

Contrasting inequality of green spaces and buildings between cities in China

The significance of sustainable urban development is unequivocally emphasized within the framework of the United Nations Sustainable Development Goals (SDGs), with urban green spaces playing a pivotal role. However, there exists uncertainty concerning the rationalization of urban land use structures in China. This study used a new indicator, the ratio of construction land area to green space area (UGI), designed to investigate both the static and dynamic change of green space from 2000 to 2020. The analysis revealed that urban construction land expanded by a factor of 1.85 between 2000 and 2020, while China's overall UGI saw a substantial decline of 77.8%. Furthermore, the text identified three different patterns of change in ΔUG (the changes in construction area to green space), with Datong city having a value of 1,580, where the urban development was seriously unbalanced. Notably, results indicated the proportion of secondary industry and the GDP had a significant correlation with UGI and the inequality is more significant within the province of Sichuan and Hunan. In conclusion, the findings emphasize the imperative for cities with high UGI values to increase their financial investments in green spaces, thereby expediting progress towards the realization of the SDGs.

Unveiling the Spatial-Temporal Characteristics and Driving Factors of Greenhouse Gases and Atmospheric Pollutants Emissions of Energy Consumption in Shandong Province, China

As the largest energy-consuming province in China, Shandong faces the dual task of greenhouse gas (GHG) reduction and atmospheric pollution control. Based on the latest activity data and updated emission factors, this study establishes a high-resolution emission inventory (5 km × 5 km) for GHGs and main atmospheric pollutants from the energy consumption sectors of Shandong Province from 2010 to 2021, quantifies the relationship between social economic factors and GHGs and atmospheric pollutants emissions using the expanded stochastic environmental impact assessment (STIRPAT) model, and forecasts the future emission trend with the help of the scenario analysis method. Results indicate that the electricity and transportation sectors are the main contributors to all pollutants. Spatially, the high value of pollutants is mainly concentrated in the urban agglomerations of central and eastern Shandong. Up to 72% of GHGs and 50% of air pollution emissions are attributed to the top 10% of emission grids. Emission peaks occur mainly in summer and winter due to straw burning, increased utilization of temperature-controlled facilities, and expansion of plant capacity. Population, energy consumption, the proportion of secondary industry, and energy consumption intensity are the most significant influencing factors for pollutant emissions. Scenario analysis results indicate Shandong province can reach its carbon peak in 2027 without sacrificing population growth or economic progress.

Study on carbon emission reduction countermeasures based on carbon emission influencing factors and trends.

The carbon mitigation response encompasses a variety of strategies aimed at mitigating greenhouse gas emissions resulting from human activities. These measures are crafted to address the challenges posed by climate change and facilitate the transition of businesses towards a low-carbon paradigm. Leveraging the analytical outcomes of the extended STIRPAT model and the PSO-BP prediction model, this paper suggests countermeasures for reducing carbon emissions in China's metal smelting industry. The overarching objective is to contribute to China's attainment of the "dual carbon objectives." The study identifies key factors influencing carbon emissions in the metal smelting industry, ranked in descending order of sensitivity: population, coal consumption, urbanization rate, total metal production, carbon intensity, proportion of secondary industry, and GDP per capita. Results from three established scenarios-namely, low carbon, standard, and high carbon-indicate a consistent decline in carbon emissions from China's metal smelting industry over the next 15 years. This research not only enhances the findings of existing studies on carbon emissions in the metal smelting sector but also introduces an innovative approach to carbon emission reduction within China's metal smelting industry.

Impact of economic development on soil trace metal(loid)s pollution: A case study of China

Recently, intensive anthropogenic activities, while promoting economic growth, have also exacerbated soil trace metal(loid) (TM) pollution. To explore the impact of economic development on soil TM pollution, a time-weighted method was introduced to calculate the average concentrations of eight TMs in Chinese topsoil from 2001 to 2020, and panel data on TMs and economic factors of 31 provinces were used for regression analysis. The results revealed that the average concentrations of soil TMs all exceeded their respective soil background values. Meanwhile, the spatial distribution of soil TMs was characterized by obvious regional heterogeneity, with economically developed areas being heavily polluted and having high ecological risks. In addition, the results derived from panel data models showed that the relationship between soil TM pollution and economic development in China presented a continuous growth curve, but with an N-shaped pattern in eastern China, a U-shaped pattern in central China, and a positive linearity in western China. Four control variables were also introduced to evaluate their impact on TM pollution, and the results indicated that the proportion of secondary industry and the road area per capita were the major influencing factors. Ultimately, the inflection point estimation results suggested that the soil TM pollution level will increase in eastern China, central China and western China with ongoing economic growth. Our findings contribute to the current understanding of the relationship between soil TM pollution and anthropogenic activities, and provide a scientific basis for adjusting and planning industrial development and layout according to the characteristics of soil TM pollution.

Driving factors and clustering analysis of expressway vehicular CO2 emissions in Guizhou Province, China.

Expressways are essential for intercounty trips of passenger travel and freight mobility, which are also an important source of vehicular CO2 emissions in transportation sector. This study takes the expressway system of Guizhou Province as the research objective, and establishes the multi-year expressway vehicular CO2 emission inventories at the county level from 2011 to 2019. We employ the extended STIRPAT model incorporating ridge regression to identify driving factors from six different aspects, and then utilize the affinity propagation cluster method to conduct the differentiation research by dividing Guizhou's counties into four clusters. Based upon clustering analysis, localized and targeted policies are formulated for each cluster to reduce expressway vehicular CO2 emissions. The results indicate that generally: (1) Guizhou's expressway vehicular CO2 emissions manifest a continuously upward trend during 2011-2019. Small-duty passenger vehicle (SDV), light-duty truck (LDT), and heavy-duty truck (HDT) contribute to the largest CO2 emissions in eight vehicle types. (2) GDP and population are the foremost two positive driving factors, followed by urbanization rate and expressway length. The proportion of secondary industry is also a positive driver, but that of tertiary industry exhibits an opposite effect. (3) Regional disparity exists in four county clusters of Guizhou Province. Efficient policies are proposed, such as improving the layout and infrastructure of transportation hubs, promoting multimodal integration, and implementing industrial upgrading as per regional advantages. Sustainable expressway vehicular CO2 emission reduction is realized from both the source of industry and low-carbon modes of transport.

Carbon intensity constraint, economic growth pressure and China's low-carbon development

Within the context of promotion tournaments among local governments, the management of economic growth goals plays a crucial role in China's economic development. Despite China's rise as the second-largest economy globally, it has also emerged as the largest emitter of carbon emissions. Since the implementation of the 12th Five-Year Plan in 2011, the Chinese central government has made carbon intensity targets mandatory indicators for national economic development. This has prompted local governments to pursue low-carbon growth and adjust their economic growth targets (EGT) to comply with carbon intensity constraints. In this study, a sample of 282 prefecture-level cities in China is used to empirically examine the impact of carbon intensity constraints on total factor carbon emission efficiency (TCE) using the intensity difference-in-differences (DID) framework. The study also emphasizes the role of the transmission channel for economic growth pressure (EGP). The findings of the study reveal several key results. Firstly, the implementation of carbon intensity constraints leads to an average increase of 8.24% in total factor carbon emission efficiency (TCE), which is supported by robustness tests, parallel trend analysis, and placebo tests. Secondly, these constraints result in an average decrease of 0.1828 in local governments' economic growth targets (EGT) and a reduction of 0.1269 in economic growth pressure (EGP). Thirdly, cities with a higher proportion of secondary industry experience a more significant mitigation effect, although the promotion of provincial EGT hinders this effect. Fourthly, synergistic policies can effectively promote low-carbon development, and government expenditure on technology and marketization can facilitate a positive relationship between carbon intensity constraints and TCE. Lastly, the effects of carbon intensity constraints vary across the east, middle, and west regions, suggesting the presence of heterogeneity. The article proposes a shift in the assessment of lower governments by superior governments, from growth assessment to low-carbon growth assessment.

The impact of double carbon goals on industrial structure in a region of China

As an extensive and profound systemic change, carbon emission reduction is driving the reform and transformation of China’s economic and industrial structure. Firstly, a variable fractional order model is used to predict carbon emissions under different carbon reduction policy scenarios. Based on the analysis of different scenarios, the non equal order adjacent accumulation model was established to analyze regional industrial structure. Empirical research finds that with the continuous increase of policy strength, the proportion of primary industry structure and tertiary industry structure in Hebei will increase, and the proportion of secondary industry structure will decrease; the proportion of primary industry and secondary industry in Tianjin will increase, and the proportion of tertiary industry will decrease; the industry structure in Beijing is relatively stable. Therefore, the implementation of double carbon target should be scientifically judged and advance gradually in regular order, and an implementation strategy of multi-dimensional balance and dynamic optimization should be sought. The research conclusions are conducive to providing scientific guidance for policy formulation and helping to achieve the double carbon goal.

Spatiotemporal Variation and Development Stage of CO2 Emissions of Urban Agglomerations in the Yangtze River Economic Belt, China

As the world’s largest developing country, China has played an important role in the achievement of the global CO2 emissions mitigation goal. The monitoring and analysis of CO2 emissions in the Yangtze River Economic Belt (YREB) urban agglomerations is strategic to the carbon peak and carbon neutrality in China. In this paper, we revealed the spatial and temporal variations of CO2 emissions in Cheng-Yu urban agglomeration (CY-UA), Yangtze River Middle-Reach urban agglomeration (YRMR-UA), and Yangtze River Delta urban agglomeration (YRD-UA) in YREB and investigated the carbon emission development stage of YREB urban agglomerations. Particularly, a carbon emission development stage framework that considered the relationship between economic growth and carbon emissions was built based on Environmental Kuznets Curves (EKCs). Meanwhile, multiscale geographically weighted regression (MGWR) was used to analyze the impact of different influencing factors, including population (POP), GDP per capita (GDPPC), the proportion of secondary industry (SI), carbon emission intensity (CI), and urbanization (UR), on the CO2 emissions of three urban agglomerations. The results illustrate the following: (1) The CO2 emissions of YREB urban agglomerations decreased, with YRD-UA having the highest CO2 emissions among the three urban agglomerations and contributing 41.87% of YREB CO2 emissions in 2017. (2) CY-UA, YRMR-UA, and YRD-UA reached the CO2 emissions peak in 2012, 2011, and 2020, respectively, all of which are at the low-carbon stage. (3) POP and GDPPC show the greatest impact on the CO2 emissions of the three YREB urban agglomerations.

Inferring the role of fiscal decentralization and carbon taxes through the ARDL approach

Abstract This paper first introduces the algorithmic steps of the ARDL model, explores the long-run relationship of variables using the cointegration test and estimates the long-run relationship, and then builds an error correction model to explore the short-run relationship in the model to estimate the model coefficients. Finally, we analyze the changes in carbon emissions and the relationship between carbon emissions and economic growth in China in time and space, respectively, and use the ARDL model to empirically test the correlation between fiscal decentralization and carbon tax Between 1978 and 2004, the growth rate of the economy remained above 10%, and the growth rate of carbon emissions also increased rapidly at this time, and carbon emissions were positively correlated with economic growth, and the growth rate of economic growth was positively correlated with the growth rate of carbon emissions. The growth rate of carbon emissions is positively correlated with economic growth, and the trend of the growth rate of economic growth and carbon emissions is roughly the same. The coefficient of fiscal decentralization degree on carbon tax expenditure is inhibited at a 1% significant level. The coefficient of the industrial structure plays a catalytic role in carbon tax expenditure at a 1% significant level, and for every 1% unit decrease in the proportion of secondary industry to GDP, carbon tax expenditure decreases by 2.473%. The implementation of a carbon tax can achieve the goal of energy saving and emission reduction, but we should also pay attention to the possible counter-effects of a carbon tax on economic development and reasonably levy carbon tax to make the economy and environment reach a balanced state.

The impact of ultra-high voltage projects on carbon emissions in China.

The inter-regional ultra-high voltage (UHV) projects are crucial for power systems. Carbon emissions associated with the power sector cannot be ignored. In this paper, based on the panel data of 198 prefecture-level cities in China from 2009 to 2019, a multi-period difference-in-difference model is developed for the first time to examine the impact of UHV projects on carbon emissions. Empirical results show that UHV projects increase overall carbon emissions. This impact can also be achieved through the mechanisms of the scale of power generation and the level of economic development. Heterogeneity research demonstrates that the carbon emissions issue of UHV projects is more significant in cities with low levels of economic development and a large proportion of secondary industries. Extensibility research shows that UHV projects have a more significant impact on carbon emissions in power-sending cities than that in power-receiving cities. The more clean power the UHV transmission lines transmit, the less impact on carbon emissions. This study not only enhances our understanding about the impact of UHV projects on carbon emissions, but also provides inspiration for the low-carbon pathway transition.

Spatiotemporal dynamics of near-surface ozone concentration and potential source areas in northern China during 2015-2020.

Near-surface ozone (O3) pollution has become one of the main factors hampering urban air quality in northern China. However, on a spatiotemporal scale, dynamic transport paths and potential source areas of O3 in northern China are ambiguous. In addition, we suspect that the contribution of transportation activities to urban O3 concentrations developed in northern China may be underestimated. In this study, the HYSPLIT, PSCF, CWT and GTWR model were used to study the transmission paths, potential source areas and driving factors of urban O3 concentration on a spatiotemporal scale. The average annual concentration of surface O3 (the 90th percentile of MDA8) was 172 ± 29μg/m3 in northern China from 2015 to 2020. In terms of inter-annual variation, the urban O3 concentration increased from 2015 to 2018, and decreased after 2018. On the spatial scale, the areas with high O3 concentration were mainly clustered in industrial cities (Tangshan, Baoding, Shijiazhuang, Xingtai and Handan). During the study period, the area with high O3 concentration in northern China shifted from northwest to southeast. From 2015 to 2020, the influence of long-distance air mass trajectories from Xinjiang and Siberi on airflow transport in Beijing city dominates (78.60%) The average percentage of short-distance transport trajectories from Shandong Peninsula region is about 21.40%. The core potential source areas of O3 pollution shifted from northwest to southeast, but the contribution to O3 pollution in Beijing gradually weakened during the same period. Temperature and relative humidity were the main meteorological driving factors affecting O3 concentration in the study area, while population density, the proportion of secondary industry in GDP, industrial smoke (dust) emissions, and passenger traffic were the main non-meteorological factors. During the period study, the influence of industrial and traffic emissions had a more significant impact on O3 concentration in northern China, which will require that more attention be paid to emission mitigation in the regional industrial and passenger transportation sector, as well as the joint prevention and control of O3 pollution in northern China in the future.

Long-term energy-environment-economic programming under carbon neutrality target: a study on China’s regional energy transition pathways and CO2 mitigation strategies

ABSTRACT Carbon neutrality is one of the key issues in mitigating global climate change. The carbon neutrality process accelerates the global energy transformation from fossil energy to renewable energy. Among the main indicators related to carbon neutrality proposed by the Chinese government, those directly related to energy include carbon emission intensity, the proportion of non-fossil energy consumption, and the total installed capacity of wind and solar power generation. It can be seen that the transformation of energy development is crucial for realizing carbon neutrality. Meanwhile, the correlation between carbon neutrality and economic development is not antagonistic. On the contrary, carbon neutrality can not only enhance the quality and efficiency of traditional industries but also improve the overall development of society. Therefore, the coordinated and sustainable developments of the energy - economy- environment (3E) system is the priority for achieving the target of carbon neutrality. However, the impact of carbon neutrality on regional development requires long-term consideration, which increases the difficulties of comprehensive planning. In this study, a long-term comprehensive programming model of regional energy and economic development under carbon peaking and carbon neutrality targets was established. A carbon neutrality assessment indicator (CNAI) framework was constructed to assess the effect of carbon neutrality actions in different regions. Meanwhile, a LEAP model was developed to simulate energy demand and carbon emissions. The multi-target weights determined by assessment results of CNAI and the parameters range set by LEAP model were the basis of the multi-objective optimization model to support regional development. Furthermore, the proposed method was applied to Shaanxi province in China, which offered quantitative targets and suggestions for energy structure transformation and industrial structure adjustment. The results show that coal and oil will account for 6.25% of the total energy consumption and the proportion of secondary industry will drop to 25.40% by 2060. The method established in this study provides a new scientific approach for resolving multi-objective trade-offs in long-term programming and determining a reasonable optimization range for regional development under climate mitigation policies.

Exploring the effect of human capital on carbon emissions: evidences from 125 countries.

Human capital (HC) plays a crucial role in economic growth, and also has a considerable effect on environmental performance, including carbon emissions (CEs). Existing studies have drawn inconsistent conclusions on whether and how HC affects CEs, and most of them conduct case studies of a certain country or several countries with similar economic backgrounds. In order to accurately determine the effect and the influence mechanism of HC on CEs, this research conducted an empirical study by applying econometric method and the panel data of 125 countries over the period 2000-2019. The empirical results indicate that there is an inverted U-shaped nexus between HC and CEs of full sample countries, revealing that HC will increase CEs before turning point and decrease CEs after turning point. From a heterogeneity perspective, this inverted U-shaped nexus only exists in high and upper-middle income countries, while is not supported in low and lower-middle income countries. This study further disclosed that HC can affect CEs by the mediating effects of labor productivity, energy intensity, and industrial structure from a macro perspective. Specifically, HC will increase CEs by promoting labor productivity, while decrease CEs by reducing energy intensity and the proportion of secondary industry. These results can provide important references for governments of different countries to make tailored carbon reduction policies according to the mitigation effect of HC on CEs.

Temporal and Spatial Distribution of Ozone and Its Influencing Factors in China

Tropospheric ozone (O3) pollution has emerged as a significant concern, as it can adversely influence human health, daily activities, and the surrounding environment(The following tropospheric O3 is referred to as O3). Research on the societal contribution to O3 primarily concentrates on the generation mechanisms and chemical processes, with limited studies examining the influence of social and economic activities on O3 at a national scale. In this investigation, spatial econometric models, random forest models, and geographically weighted regression (GWR) were adopted for assessing the effects of meteorological, natural, and socioeconomic factors on O3 concentration throughout the country. The spatial error model (SEM) revealed that precipitation, temperature, wind direction, per capita GDP, RD project funding, and SO2 were the primary factors influencing O3 concentration in China, among which precipitation had the strongest effect on O3, followed by temperature and SO2. Subsequently, the GWR model was utilized to demonstrate the regional differences in the impacts of precipitation, NOx, secondary industry proportion, and electricity consumption. In central and western regions, such as Jiangxi, Guangxi, and Guizhou, precipitation, NOx, and power consumption were the leading factors contributing to severe O3 pollution. The secondary industry proportion substantially affected O3 pollution in the Beijing-Tianjin-Hebei region, indicating that this sector played a crucial role in the region’s economic growth and contributed to elevated O3 concentrations. Meteorological, natural, and socioeconomic factors exhibited a lesser influence on O3 pollution in most eastern regions compared to central and western regions. This study’s findings identified the primary contributors to O3 pollution and provided a scientific basis for developing strategies to mitigate its impact.

Analysis of Carbon Emissions and Influencing Factors in China Based on City Scale

Assessing regional carbon emissions and their relationship with socio-economic conditions is very important for developing strategies for carbon emission reduction. This study explored the impact of the proportion of non-fossil energy, the land development degree, the urbanization rate of permanent residents, the proportion of secondary industry, per capita GDP, and per capita construction land area on per capita CO2 emissions in 339 prefecture-level and above cities in China (excluding some cities in Xinjiang, Hong Kong, Macao, and Taiwan). A Bayesian belief network modeling carbon emissions was constructed to identify the global effects of various factors on per capita CO2 emissions, and multiscale geographically weighted regression was used to analyze their local effects. The results showed that first, per capita CO2 emissions of cities in China increased from the south to the north and decreased from the eastern coast to the inland region. Second, globally, the sensitivity of per capita CO2 emissions to various factors from high to low was in the order of per capita construction land area>per capita GDP>urbanization rate of permanent residents>land development degree>proportion of secondary industry>proportion of non-fossil energy. Third, locally, the direction of the spatial relationship between each factor and per capita CO2 emissions was consistent with the global relationship, and there was spatial heterogeneity in the strength of the relationship. Finally, clean energy, decarbonization technologies, saving and intensive use of land, and green living were effective ways to achieve the dual-carbon goal.

Study on the Impact of Collaborative Agglomeration of Manufacturing and Producer Services on PM2.5 Pollution: Evidence from Urban Agglomerations in the Middle Reaches of the Yangtze River in China

In this paper, using panel data of 28 cities in the middle reaches of the Yangtze River from 2003 to 2020 as the research sample, we built a dynamic spatial Durbin model based on the STIRPAT (stochastic impacts by regression on population, affluence, and technology) model and conducted an empirical study on the impact of the coordinated agglomeration of manufacturing and producer services on particulate matter (PM) 2.5 pollution. The results show a significant positive spatial spillover effect of PM2.5 pollution in the middle reaches of the Yangtze River. The coordinated agglomeration of manufacturing and producer services in the urban agglomerations there is conducive to reducing PM2.5 pollution. Similar to the inverted-U curve of the classic environmental Kuznets curve hypothesis, there is a significant inverted-U curve relationship between PM2.5 pollution and economic growth in urban agglomerations in the middle reaches of the Yangtze River. The proportion of coal consumption, the proportion of secondary industry, and the urbanization level are significantly and positively correlated with PM2.5 pollution in urban agglomerations in this area. Technological innovation, environmental regulation, and annual average humidity play an important role in addressing the PM2.5 pollution and spatial spillover effect. Industrial structure and technological innovation are the main ways for the coordinated agglomeration of manufacturing and producer services to affect PM2.5. The research conclusion can be of great practical significance to optimize the regional industrial layout, control PM2.5 pollution, and establish a sustainable development policy system in the middle reaches of the Yangtze River in China.