Mei Song’s research while affiliated with China University of Mining and Technology - Beijing and other places

The development and implementation of county carbon control action plans in the Yellow River Basin (YRB) are crucial for realizing the “dual carbon” goals and modernizing national governance. Utilizing remote sensing data from 2001 to 2020, this study constructs a light-carbon conversion model and a carbon footprint model to simulate the carbon footprint of county energy consumption in the YRB. Employing spatial autocorrelation and spatial Durbin models, the study examines the temporal-spatial evolution characteristics and spatial effect mechanism. The results show that: (1) The county carbon footprint increased year by year. The distribution of the high carbon footprint is consistent with that of energy-intensive areas. The carbon cycle system is significantly unbalanced, and the counties with carbon deficit spread inland. (2) The carbon footprint exhibits significant spatial dependence, and the high carbon spillover effect is significant. Regional joint prevention and control strategy is essential to control the carbon footprint. Otherwise, the inter-regional carbon leakage effect may occur. (3) The current stage of economic development and industrial structure upgrading is not conducive to low-carbon development. Because of the energy rebound effect, technology development has not played the expected emission reduction effect. Nevertheless, the technology level and residents’ living standard are critical factors in reducing the carbon footprint. Government intervention, urbanization, human capital, and agricultural energy inputs increase the carbon footprint.

Population aging presents a major challenge to China's economic and social development. While some research has addressed the relationship between population aging and energy consumption or generalized carbon emissions, its impact on living carbon emissions has been less explored. This study uses provincial panel data from the Yellow River Basin (2006–2020) and employs a threshold regression model to analyze the relationship between population aging and living carbon emissions. A Multiple Mediation Effect Model is also applied to explore the mechanisms behind this relationship, with a comparative analysis between urban and rural areas. The results indicate a U‐shaped nonlinear effect of population aging on living carbon emissions, which are initially inhibited and then promoted, with different “turning points” for rural and urban regions. As income levels rise, population aging has a marginally increasing effect on living carbon emissions. However, upgrading the consumption expenditure structure, expanding family size, and increasing clean energy consumption can help mitigate the promoting effect of aging on emissions. The mediation model identifies four pathways through which population aging influences living carbon emissions, with income level and consumption expenditure structure playing key roles. This study offers policy insights for addressing urban‐rural disparities and fostering sustainable regional development.

Classifying emission reduction zones on different scales has important implications for the ecological protection and high‐quality development of the Yellow River Basin. Based on remote sensing data and a light‐carbon conversion model, carbon footprints at provincial, municipal, and county scales in the Yellow River Basin are measured. The spatiotemporal evolution critical paths of carbon footprints at the three spatial scales are compared and classified into different zones using spatiotemporal evolution analysis methods. The conclusions are as follows: (1) The carbon footprint increased over the years. The spatial distributions of carbon footprints at the three scales are not only consistent but also different. The study of carbon footprints at the county scale is more conducive to the summary of the spatiotemporal evolution and the formulation of detailed emission reduction schemes. (2) Four provinces, 48 cities, and 373 counties are designated as a “core protected zone”; three provinces, 29 cities, and 177 counties are designated as a “strictly governed zone”; one province, 12 cities, and 47 counties are designated as a “key restricted zone”; four cities and 39 counties are designated as an “alert diffusion zone.” (3) The agglomeration expansion trend and the spillover effect of high‐carbon footprint units at the county scale are more obvious. Further enhancement of the path‐locking characteristics of the carbon footprints of counties will make governance more difficult. Effective governance of carbon footprint at the county scale is of urgent concern. These results provide scientific evidence for multiscale carbon emission control and zoning policy formulation in the Yellow River Basin.

As an important part of regional coordinated development, the high-quality development of the Yellow River Basin has become a national strategy. It is imminent for resource-based cities to perform a high-quality transformation. The analysis of carbon emission efficiency in the Yellow River Basin includes the examination of spatiotemporal evolution characteristics and the main driving factors. This is done by utilizing the super-efficiency SBM-DEA and panel Tobit regression models, with the assistance of night light data. Our findings are as follows: (1) Carbon emissions continue to grow. The “Jiziwan” basin is an area where plenty of high-emitting cities agglomerate. The carbon emission of resource-based cities presents a W-shaped pattern in time. (2) In time, the carbon emission efficiency follows a U-shaped curve. Spatially, the carbon emission efficiency in the middle reaches is comparatively low, whereas it is relatively high in both the upper and lower reaches. And that in high carbon-emitting resource-based cities are in the low to medium range. (3) Carbon emission efficiency has a significant negative relationship with energy intensity, urbanization rate, and population density and a significant positive relationship with industrial proportion. Energy intensity is the most direct driving force. That is to say, we can increase carbon emission efficiency effectively by reducing energy intensity.

The proposal of the high-quality development strategy of the Yellow River Basin is of great significance for accelerating industrial agglomeration. This study takes 49 prefecture-level cities in the Yellow River Basin as the research object. Based on the panel data from 2006 to 2018, we used the location quotient to calculate the manufacturing agglomeration, the producer service industry agglomeration and the synergistic agglomeration in the basin. The spatial Dubin model of the impact of the three types of agglomeration on the economic growth in the basin was constructed. The Yellow River basin was divided into upstream, midstream and downstream to explore the regional heterogeneity of the impact of the industrial agglomeration on the economic growth. The result showed that (1) the economic development of the Yellow River Basin has a spatial overflow. The economic improvement of the surrounding cities promotes local economic growth—the manufacturing agglomeration, producer service industry agglomeration and synergistic agglomeration all promote economic growth. The effect of the manufacturing agglomeration is more significant than the others. (2) The impact of the industrial agglomeration on the economic growth in the Yellow River Basin presents an evident regional heterogeneity, and the magnitude and direction of the action vary in the different regions.

The purpose of this paper is to help better understand the problem of energy poverty; to grasp the research context, evolution trends and research hotspots of energy poverty; and to find clues from research on energy poverty. In this paper, we use the scientific quantitative knowledge graph method and CiteSpace software to analyze 814 studies in the WOS (Web of Science) and CNKI (China National Knowledge Infrastructure) databases, such as a literature characteristic analysis, a core author and research institution network analysis, a research hotspot analysis, research trends and a frontier analysis. The results show that the specific connotations of energy poverty are different between developed countries and developing countries. In developed countries, energy poverty is mainly manifested in the affordability of energy consumption, while in developing countries, energy poverty is manifested in the availability of energy. The causes, impacts and solutions of energy poverty are the focus of CNKI and WOS literature, and their perspectives of the impacts and solutions are relatively consistent. However, in terms of the causes, scholars of WOS discuss the energy supply side and the demand side, while scholars of CNKI mainly analyze the energy demand side. The quantitative evaluation system of energy poverty has not been unified, which restricts the depth and breadth of energy poverty research. Topics such as the expanding scope of research objects; the interaction among energy poverty, the “two-carbon” target and other macro factors; the complex and severe energy poverty situation following the COVID-19 pandemic and the outbreak of the war in Ukraine; and the ways to solve the energy poverty problem in the context of China may become the focus of research in the future. This study provides an overview for researchers who are not familiar with the field of energy poverty, and provides reference and inspiration for future research of scholars in the field of energy poverty research.

Considering China's goal of carbon neutrality, understanding the influence path and dynamic correlation between electricity consumption and relevant factors is essential for formulating a reasonable electricity development strategy and promoting high‐quality socio‐economic development. Based on panel data of 30 provinces in China from 2000 to 2019, this study adopts the panel VAR model for research, with four notable findings. (1) Economic development, urbanization, and the urban–rural income gap directly affect electricity consumption. Industrial structure upgrading can affect electricity consumption through economic development and urbanization. Electricity consumption only directly affects economic development, whereas the effect on the other three variables could be transmitted through economic development. (2) Electricity consumption first inhibits and then promotes industrial structure upgrading, and it inhibits the urban–rural income gap. (3) Industrial structure upgrading first promotes and then inhibits electricity consumption. The urban–rural income gap inhibits electricity consumption. (4) Industrial structure upgrading and the widening income gap might hinder economic development through the adverse effect on electricity consumption. Based on these findings, several policy suggestions are proposed.

The sustainable development of resource-based cities is vital to China's high-quality development. Based on the support-pressure framework, this study simplifies the city system into an economy–society subsystem (ESS) and a resource–environment subsystem (RES), and measures the economy social developmental level (ESDL) and resource environmental carrying capacity (RECC) of China's 116 resource-based cities using the improved entropy-TOPSIS model. Then, it applies the coupling coordination degree (CCD) and relative development models to explore their coupling coordination relationships and relative developmental types. The results are as follows. (1) The ESDL and RECC of China's resource-based cities have improved significantly, and there is a large divergence between cities in different regions, development stages, and dominant resource types. (2) The CCD between the ESDL and RECC of China's resource-based cities is still not ideal, and no city qualifies for the high coordination category. (3) Overall, the RECC lags behind the ESDL, and the cities with a lagging ESDL are concentrated in the western and northeastern regions. Based on these conclusions, three specific suggestions are put forth. This study may provide a scientific reference for the Chinese government to formulate a sustainable development plan for resource-based cities. Integr Environ Assess Manag 2022;18:770–783. © 2021 SETAC KEY POINTS Based on the support-pressure framework, we constructed a comprehensive evaluation index system of the urban economy social developmental level (ESDL) and resource environmental carrying capacity (RECC). We measured the ESDL and RECC in 2013 and 2018 of China's 116 resource-based cities using the improved entropy-TOPSIS model, and explored their coupling coordination relationships and relative developmental types. Significant improvement occurred in both subsystems of resource-based cities in China in 2013–2018; moreover, transformation performance of different resource-based cities is diverse, no resource-based city meets the criteria for the high coordination category, and the RECC overall lags behind the ESDL. We proposed three recommendations for the transformation of Chinese resource-based cities: strengthen the classification guidance, summarize the “Chinese transformation mode” as soon as possible, and cultivate new economic growth poles.

Coordinated development of new high-tech industries and traditional industries is crucially important for economic growth and environmental sustain-ability, and it has become a focus of academic and governmental bodies. This study establishes the comprehensive evaluation index system of high-tech industries and traditional industries, and uses the method of principal component analysis, coupling and coupling coordination degree model to determine the level of industry coordinated development. Then, Pearson correlation test is used to further analyze the correlation between regional industrial coupling coordination and carbon intensity of the seven provinces in the Bohai Rim Economic Circle (BREC). The results are as follows. (1) There is a negative correlation between industrial coupling coordination and carbon intensity. (2) The degree of industrial coordination of Beijing, Tianjin, and Shandong is significantly higher than other provinces in the BREC, as both the high-tech industries and traditional industries of these three provinces have reached a high level of development and achieved high coupling. The high-tech industries of the three provinces show positive changes, whereas the traditional industries show negative changes, which indicates that the new high-tech industries are driving the upgrading of the traditional industries by the application of high technologies. (3) From 2011 to 2016, the number of provinces with a low degree of high-tech and traditional industrial coordination fell from three to one. The traditional industries in Hebei and Inner Mongolia have been upgraded by strengthening their technological innovation with the introduction of rapid high-tech industrial development. These findings are a useful reference for regional industrial coupling coordination and carbon emission reduction.

The Bohai Economic Rim (BER) is a momentous economic growth district with rapid development in northern China, but the environmental problems there have also become prominent. In 2017 the BER's carbon emission intensity outclassed the national average, the emission reduction situation was also grim. For clarifying the influence mechanism of the economy on carbon emission intensity, this paper explores the spatiotemporal regularity, the spatial correlation, and the spillover effect in carbon emission intensity employing the Moran index and the spatial Durbin model. The results indicate that the carbon emission intensity in the BER decreased year-by-year from 2006 to 2017. Shanxi and Inner Mongolia were emission hot spots, whereas Beijing and Tianjin were emission cold spots. And the Moran's I values all passed the significance test, which verified the spatial correlation of the carbon emission intensity in the BER is significant. Urbanization, energy intensity, population density, and industry structure have a biggish impact on such spatial distribution of the carbon emission intensity. The direct effect coefficient of the energy intensity is the highest, and the spillover effect of the industry structure is the most significant. Finally, this paper puts forward suggestions on the formulation of regional coordinated carbon reduction programs in the BER.