Explore the vast range of titles available.

The industrial sector is the backbone of China’s national economy. The industrial carbon emission efficiency (ICEE) of China is directly related to the achievement of carbon emission reduction targets. This paper reports on the use of the minimum distance (min-SBM) method to determine the ICEE of 30 provinces in China during 1998–2015, as well as the use of a spatial econometric method to investigate the convergence and influencing factors of the regional ICEE. The results indicate significant regional differences in the ICEE. The provinces with higher average values of ICEE are located in the eastern coastal areas, whereas the provinces with lower average values of ICEE are located in the central and western inland regions. The results of the spatial autocorrelation index reveal that China’s inter-provincial ICEE exhibits significant spatial autocorrelation characteristics, and its spatial distribution demonstrates a certain regularity. The local indicators of spatial association diagram further illustrate that most provinces in China have high and low agglomeration values. With the introduction of the spatial effect, the absolute and conditional convergence rates increase. In addition to the non-significant industrial structure effect, the level of economic development, foreign direct investment, technological progress, and government intervention demonstrate a positive impact on the ICEE convergence, whereas the energy consumption structure has a negative impact. This work investigates the cause for the regional gap in China’s current ICEE. Suggestions for improving the efficiency of China’s industrial carbon emissions and narrowing the regional gap are provided, which serve as a reference value for China to achieve the peak of carbon dioxide emissions before 2030.

The sustainable development of China’s economy is bottlenecked by resource shortage and environmental pollution. As the leading resource consumer and pollutant source, the industrial sector needs to improve its energy efficiency. This paper establishes a super epsilon-based measure (Super-EBM) model with bad outputs like environmental cost and evaluates the industrial green total-factor energy efficiencies (IGTFEEs) of 30 provinces in China during 2000–2017. Unlike previous research, the main contribution of this paper is to choose four environmental pollutants as bad outputs (industrial carbon dioxide, industrial sulfur dioxide, industrial chemical oxygen demand, industrial solid waste). By contrast, the previous studies mostly only take one environmental pollutant as bad output, i.e., the bad outputs are not fully measured. Then, the spatiotemporal dynamics and spatial correlations of the IGTFEEs were analyzed, and the influencing factors of IGTFEE were examined empirically with a spatial econometric model. Finally, this paper adopts generalized method of moments (GMM) to solve the endogenous problem, trying to assure the robustness of estimation results. The results show significant provincial differences in IGTFEE. Most eastern coastal provinces achieved satisfactory IGTFEEs, while most inland provinces had undesirable IGTFEEs. Eastern region achieved the highest IGTFEE, followed by central region; western region had the lowest IGTFEE. The IGTFEE improved over time in some provinces while worsened greatly in some provinces. The IGTFEE in most provinces need to be further improved. Global Moran’s I values indicate that the provincial IGTFEEs were clustered in space, rather than randomly distributed. Local indication of spatial association (LISA) map reflects significant local spatial clustering of provincial IGTFEEs. In addition, IGTFEE is significantly promoted by industrial structure, technological innovation, human capital, opening-up, and energy structure yet significantly suppressed by ownership structure and environmental regulation. Considering the endogeneity, GMM results show that the estimation results of the model were robust. Specific policy recommendations include vigorously developing high-tech industries, deepening state-owned enterprises reform, diverting more funds to research and development, cultivating versatile talents, introducing environmentally-friendly foreign capital, accelerating the implementation of clean energy development strategy, and widening the fund channels of pollution control investment.

Facilitating the coordinated and effective progress of green finance (GF) and ecological efficiency (EE) stands as a potent approach to support our nation in attaining sustainable development goals. This paper Utilized panel data encompassing 84 cities in Western China spanning from 2007 to 2021, this paper empirically analyzes the spatio-temporal characteristics and driving factors of the coupling coordination degree between green finance and ecological efficiency (CCD-GFEE) in western cities. The findings indicate that: (1) The level of GF demonstrates a rising trajectory, with significant regional disparities. Besides, the high level area progressively moves from the northwest to the southwest. (2) On the whole, urban EE demonstrates a relatively elevated level, but it still fails to reach DEA effectiveness. Compared to the northwest region, the southwest region has poorer efficiency. (3) The CCD-GFEE in western China showing a slight growth trend. The coupling coordination degree (CCD) in Northwest China is higher than that in Southwest China, and cities with higher CCD are primarily found in Inner Mongolia, Sichuan Province and Shaanxi Province. Within them, the CCD of Chengdu is the highest, Chongqing has achieved the largest stage leap. (4) The global Moran’s I consistently remained positive and exhibited a tendency of initially rising and subsequently falling, indicating that the spatial aggregation effect of CCD-GFEE first increased and then decreased. (5) The CCD-GFEE driving factors are examined using the spatial econometric model, and it has been observed that the impact of population size and government intervention on CCD-GFEE is negative, while the impact of industrial structure, technological progress and economic level on the coupling and coordination of CCD-GFEE is positive. Finally, the paper presents certain policy enlightenments to guide the coordinated development of GF and EE from the aspects of GF system formulation, economic construction and technological progress.

Rocky desertification poverty (RDP) refers to rural poverty triggered or exacerbated by rocky desertification, which deprives farmers of opportunities for improving their financial conditions through agricultural production. As China implements strategies including building a moderately prosperous society, targeted poverty alleviation, and rural revitalization, issues concerning RDP have become the work foci for the Chinese government and research hotspots for Chinese academic circles. This paper provides a thorough review of the history, progress, challenges and prospects based on decades of RDP studies in Southwest China. First, we review the origin, definitions and historical development trends of RDP. Over nearly four decades, research on RDP can be framed as four stages: start-up, poverty alleviation and ecological reconstruction, rocky desertification comprehensive control and green development. We find that RDP research progress appears to be directly related to the Chinese government’s policy support and decision-making in mitigating RDP. Second, previous findings are reviewed and summarised regarding four research aspects: relationship exploration between rocky desertification and rural poverty, mechanisms of rocky desertification for exacerbating poverty, strategies for eliminating RDP and anti-poverty benefit evaluations of rocky desertification control efforts. Third, the remaining challenges are identified and summarised, including the challenges of conducting theoretical research on RDP and consolidating achievements in eliminating RDP. We find that while many achievements have been made in the study of RDP in recent decades, the completeness and systematism of the theoretical system remain weak, and the guidance for empirical research is still insufficient. Finally, this research is fundamental in achieving rural revitalization in rocky desertification areas. The key to eliminating RDP lies in scientific rural transformation development. Given that the karst ecological environment in the subtropical zone is fragile, these regions should build a new rural regional economic system, abandon extensive and predatory development that sacrifices the environment and resources, and promote the transition from backwards development modes to high-quality and sustainable development. This paper will contribute to the current understanding of the issues of RDP, provide theoretical support for the realization of rural revitalization in rocky desertification areas and provide other countries with China’s experience in poverty alleviation.

Understanding the spatiotemporal heterogeneity and complex drivers of PM2.5 concentration variations has important scientific value for sustainable urban development. Taking Beijing-Tianjin-Hebei (BTH) as the research area, and using spatial analysis techniques and wavelet methods to explore the spatiotemporal heterogeneity of variations in PM2.5 concentrations, the research shows that in the past six years (2015–2020), the PM2.5 concentrations in the BTH area have a downward trend, and the mean is 59.41 μg/m3; however, the distribution pattern of PM2.5 pollution has changed very little, and the concentration in the south and southwest is still generally high. The continuous wavelet transform revealed that the PM2.5 concentrations in the study area have a short period of about a week to a half a month and a long period dominated by annual cycle. The effect of a single meteorological factor on PM2.5 concentrations is weak, but this effect has obvious spatial differentiation characteristics from coastal to inland and has a double-sided effect due to different geographical locations. The wavelet transform coherence revealed that dewpoint temperature at 2 m (TED), meridional wind at 10 m (WV) and air temperature at 2 m (TEM) are important single meteorological factors that affect the variation of PM2.5 concentrations. The multiple wavelet coherence reveals that in scenarios where two meteorological factors are combined, the combination of TED-mean wind speed (WS) is the best combination to explain the variation in PM2.5 concentrations (AWC = 0.77, PASC = 41%). In the combination of three meteorological factors, TEM-WV-WS explained the variations of PM2.5 concentrations in the BTH region to the greatest degree (AWC = 0.89, PASC = 45%). Finally, the research shows that the variations of PM2.5 concentrations in the BTH region can be better explained by a combination of 2–3 meteorological factors, among which temperature and wind are the key meteorological factors. This research will provide a new window for the multi-scale variation characteristics and multi-factor control relationship of PM2.5 concentrations in the BTH region and provide a new insight for the prevention and control of air pollution.

Studying the coordination between urban economic resilience (ER) and high-quality tourism development (HQTD) is crucial to understanding tourism’s role in responding to economic shifts and driving urban economic transformation. Using 2010–2023 panel data from the Yangtze River Economic Belt (YREB) and a “measurement—evolution—disparity—diagnosis” framework, this study examines their coupling coordination via the coupling coordination degree (CCD) model, kernel density estimation, Gini coefficient decomposition, and influence coordination force index, elucidating spatiotemporal evolution, regional disparities, and drivers. The results show: (1) YREB synergies strengthened significantly, with ER and HQTD increasingly reinforcing each other; (2) Eastern coordination levels markedly exceeded central and western ones, reflecting persistent regional imbalances; (3) Coupling coordination converged toward higher levels, with inter-city gaps narrowing. Recommendations include enhancing regional coordination, balancing ecology and economy, fostering industrial innovation, and promoting social participation. This study provides empirical support for integrated, sustainable regional economic-tourism development.

Rapid urbanization has resulted in the conversion of different land types, leading to a serious disruption of the balance of ecosystems, and the contradiction between land use and ecological security becomes increasingly severe. As a key factor affecting ecosystem services (ESs), improving the urban land green use efficiency (ULGUE) can effectively mitigate the decline in the ecosystem services function. This study tried to verify the profound impact of land use on ESs by measuring the coupling coordination degree (CCD) between ULGUE and ESs from the perspective of coupling coordination. However, the traditional CCD model suffers from volatility and non-comparable confidence levels in results. For this background, this study adopted the improved CCD model to measure the association between ULGUE and ESs in the Yangtze River Economic Belt (YREB). Also, a comprehensive spatial evolution of the CCD was further explored. The main conclusions are as follows: (a) The overall level of ULGUE in YREB showed an increasing trend with the distribution of ULGUE in most cities ranging from 0.71 to 1.00, showing a higher level overall and the spatial distribution character of “small aggregation, large distribution”; (b) The ESs of YREB from 2005 to 2020 demonstrated a decreasing trend and majority of cities’ current ecological protection efforts are lagging behind the extent of ecosystem damage caused by human activities; (c) The overall average CCD of YREB was largely at basically balanced level and the average CCD change ranges from 0.38 to 0.45 between 2005 and 2020, but the overall CCD had a strong spatial correlation strength. (4) To some extent, the improved CCD model solved the problems of volatility and credibility based on the traditional CCD results, which have strong rationality and scientific validity. Identifying the coupling and coordination characteristics and mechanisms can effectively provide theoretical support for the sustainable development of watersheds.

In order to achieve sustainable development, low-carbon economic efficiency (LCEE) is particularly important in China. Therefore, this study uses SBM-DEA model to evaluate the LCEE of 30 provinces in China from 2008 to 2017. Based on the uncoordinated coupling model, this study discusses the interaction between China’s provincial LCEE and scientific and technological development level (STDL), and uses the panel VAR model to consider the interactive response relationship between China’s provincial LCEE and STDL. The research shows that the uncoordinated coupling degree (UCCD) between the STDL and LCEE in 30 provinces showed a decreasing trend as a whole during the research period. In terms of spatial distribution, the provinces with UCCD less than 0.5 mainly concentrated in the eastern and southern provinces, gradually spread to the north, and showed positive spatial autocorrelation, with significant spatial accumulation effect. From the perspective of influencing factors, patents, urbanization level, traffic level and financial development have significant positive effects on promoting the coordinated development of STDL and LCEE. From the relationship between them, the STDL has a positive promoting effect on LCEE, but the mechanism of the two is not obvious enough. Therefore, it is necessary to emphasize the coordinated development of low-carbon economy and science and technology, and promote the development of low-carbon economy through scientific innovation.

The progress of carbon emission reduction and the effectiveness of the energy transition in the thermal power generation industry (TPI) directly impact both the quality of the implementation of China’s dual carbon goals and the broader landscape of sustainable development. To precisely analyze the core patterns of low-carbon transformation in the industry, this study overcame the limitations of existing research on the thermal power sector’s carbon emission efficiency (TPCEE) indicator system. These limitations include insufficient industry adaptability, an inadequate characterization of efficiency evolution dynamics, and an insufficient representation of regional differences. It innovatively constructed a TPCEE indicator system, focusing on the spatiotemporal evolution mechanisms and influencing factors of TPCEE. An integrated research framework of “efficiency measurement, spatiotemporal analysis, influencing factor exploration” was established. In addition, based on panel data from 30 Chinese provinces covering the period 2005–2022, empirical research was conducted using the Super-SBM model, exploratory spatiotemporal data analysis, and the Tobit model. The findings indicated that: (1) the TPCEE showed an overall fluctuating downward trend during the period of 2005–2022, and high-TPCEE areas were located primarily in North China and coastal provinces, while low-TPCEE regions were scattered in Northwest, Northeast, Central, and Southwest China. (2) Given the probability of spatiotemporal coalescence exceeding 70%, the spatial structure of the TPCEE was comparatively stable, showing distinct path dependence. (3) At the national level, the industrial structure, power generation mix, energy intensity, and degree of government intervention contributed to overall efficiency improvements. From a regional perspective, the impact of these factors on the TPCEE exhibited significant regional heterogeneity. The government may use the results as a foundation for building regional energy-saving and emission-reduction plans, as well as to encourage low-carbon transition and sustainable development in the Chinese TPI.

Eliminating poverty is the common mission of all mankind, and it is also an important task faced by many countries. Pro-poor tourism villages are an active attempt by China to use rural tourism to escape poverty. This paper aims to provide theoretical support for consolidating the results of poverty alleviation and achieving comprehensive poverty alleviation and to provide a scientific basis for policy formulation by using GIS spatial analysis to study the spatial distribution characteristics and influencing factors of 22,651 pro-poor tourism villages in China. The findings revealed that the spatial distribution of pro-poor tourism villages is roughly divided by the Hu line. Pro-poor tourism villages show an uneven agglomeration pattern and present a spatial pattern of dense southeast and sparse northwest with six high-density core areas, among which some cities in the southwest are H-H agglomeration areas. Specifically, topography, annual rainfall, endowment of tourism resources, location transportation, and policy orientation are important factors affecting the spatial distribution of pro-poor tourism villages.