Search Results (457)

This study analyzed the carbon reduction effects of water-saving irrigation based on panel data of Chinese provinces from 2010 to 2020. Carbon emissions from irrigation were calculated and decomposed using the Malmquist index and LMDI. Results indicate that, first, the accounting results show a downward trend in estimated agricultural irrigation carbon emissions over the study period under a fixed-parameter framework. The average irrigation carbon intensity exhibits a declining pattern, particularly after the mid-2010s, with differences between provinces narrowing. Second, water-saving irrigation is associated with lower levels of estimated agricultural irrigation carbon emissions within the accounting framework by improving water-use efficiency and reducing irrigation water consumption per unit area, ultimately leading to a decrease in total carbon emissions. Finally, the carbon reduction effects are more pronounced and stable in major grain-producing regions. This study highlights regional heterogeneity in the emission-accounting outcomes associated with water-saving irrigation, which may provide descriptive evidence for discussions on region-specific irrigation management under different regional contexts. Full article

Carbon emission reduction in the construction industry is pivotal for global carbon emission reduction, yet the lack of coordination mechanisms within the sector limits its effectiveness. This study examines the Yangtze River Economic Belt from 2010 to 2022, capturing the spatial and temporal evolution characteristics and key influencing factors of carbon emission efficiency in the construction industry (CEECI) to achieve coordinated emission reduction. Using the super-efficiency Slack-Based Measure (SBM) model and the Malmquist–Luenberger (ML) index, the study analyzes changes in CEECI, revealing significant regional variations: downstream, midstream, and upstream regions demonstrated average values of 1.10, 1.00, and 0.68, respectively. Resource redundancy is a major issue affecting CEECI, with energy redundancy rates exceeding 20%. The ML index indicates continuous improvement in CEECI, with technological change (TC) contributing the most to this improvement, as shown by index decomposition. Spatial analysis using Moran’s index (Moran’s I) revealed significant positive spatial autocorrelation, with distinct “high-high” (H-H) and “low-low” (L-L) clustering patterns, suggesting that regions with high CEECI positively influence their neighbors. Finally, we built a spatial econometric model to identify key influencing factors, including industrialization level, construction industry production level, energy consumption structure, human resources, and internal innovation levels, which directly or indirectly impact CEECI to varying degrees. These findings highlight the importance of regional coordination and targeted policy interventions to enhance carbon emission efficiency in the construction industry, addressing resource redundancy and leveraging technological advancements to contribute to global carbon reduction goals. Full article

The Yellow River Basin (YRB), a typical river system facing the challenge of balancing ecological conservation and economic development, offers valuable insights for global sustainable watershed governance through its forestry green transformation. Based on panel data from nine provinces in the basin from 2005 to 2022, this study constructs an efficiency evaluation indicator system for forestry green development. This system incorporates four inputs (labor, land, capital, and energy), two desirable outputs (economic and ecological benefits), and three undesirable outputs (wastewater, waste gas, and solid waste). By systematically integrating the undesirable outputs-based super-SBM model and the global Malmquist–Luenberger (GML) index, this study provides an assessment from both static and dynamic perspectives. The findings are as follows. (1) Forestry green development efficiency showed fluctuations over the study period, with the basin-wide average remaining below the production frontier. Spatially, it exhibits a pattern of “downstream > upstream > midstream”. (2) The average GML index is 0.984 during the study period, representing an average annual decline in forestry green total factor productivity of 1.6%. The growth dynamics transitioned from a stage dominated solely by technological progress to a dual-driver model involving both technological progress and technical efficiency. (3) The drivers of forestry green total factor productivity growth in the basin show profound regional heterogeneity. The downstream region demonstrates a synergistic dual-driver model of technical efficiency and technological progress, the midstream region is trapped in “dual stagnation” of both technical efficiency and technological progress, and the upstream region differentiates into four distinct pathways: technology-driven yet foundationally weak, efficiency-improving yet technology-lagged, endowment-advantaged yet transformation-constrained, and condition-constrained with efficiency limitations. The assessment framework and empirical findings established in this study can provide empirical evidence and policy insights for basins worldwide to resolve the ecological-development dilemma and promote forestry green transformation. Full article

Enhancing agricultural green total factor productivity (AGTFP) under ecological and environmental constraints is essential for advancing green agricultural development in the Beijing–Tianjin–Hebei (BTH) region. Using panel data from 13 prefecture-level cities from 2001 to 2022, this study applies a super-efficiency EBM model incorporating undesirable outputs together with the Malmquist–Luenberger index to measure AGTFP. Global and local Moran’s I indices as well as the spatial Durbin model are then employed to examine the temporal evolution, spatial disparities, and spatial interaction effects of AGTFP during 2001–2022. The findings indicate that: (1) From 2001 to 2022, the AGTFP in the BTH region grew at an average annual rate of 7.7%. This trend reflects a growth pattern primarily driven by green technological progress in agriculture, while substantial disparities in AGTFP persist across different subregions. (2) the global Moran’s I values show frequent shifts between positive and negative spatial autocorrelation, suggesting that a stable and effective regional coordination mechanism for green agricultural development has yet to be formed; (3) the determinants of AGTFP exhibit pronounced spatiotemporal heterogeneity, and the fundamental drivers of the region’s green agricultural transition increasingly rely on endogenous growth generated by technological innovation and rural human capital; (4) policy recommendations include strengthening benefit-sharing and policy coordination mechanisms, promoting cross-regional cooperation in agricultural science and technology, and implementing differentiated industrial layouts to support green agricultural development in the BTH region. These results provide valuable insights for promoting coordinated and sustainable green agricultural development across regions. Full article

As a pivotal driver of high-quality development, new quality productivity (NQP) forms an indispensable synergistic relationship with land use efficiency (LUE) for achieving regional sustainability. Based on panel data from 29 prefecture-level cities in the Central Plains Urban Agglomeration (CPUA) from 2010 to 2023, this study integrates the entropy-weighted TOPSIS method, super-efficiency Slack-Based Measure (SBM) model, Malmquist index, and fixed-effects models to systematically explore the spatiotemporal evolution of NQP and its underlying impact mechanism on LUE. Key findings reveal: (1) The comprehensive NQP index of the CPUA increased from 0.280 to 0.828, exhibiting a “stepwise rise” trend, with a spatial pattern characterized by a “core–secondary–periphery” three-tier gradient distribution. Zhengzhou, as the core growth pole, played an innovative leading role, while peripheral cities (e.g., Handan, Hebi) remained constrained by resource-dependent economic structures, with NQP indices consistently below 0.2. (2) The average LUE in the study area increased from 0.917 to 1.031. Cities within Henan Province generally performed better than those in Hebei, Shanxi, and Anhui provinces. Total factor productivity grew at an average annual rate of 16.4%, with technological progress serving as the primary driver. (3) NQP exerts a significantly positive impact on LUE, yet with notable heterogeneity: large-scale cities enhanced intensive land use substantially through technological agglomeration and industrial upgrading; cities with scarce arable land and high economic development levels effectively leveraged NQP to boost LUE; in contrast, small cities, regions rich in arable land, and areas with low economic development have not established effective synergistic mechanisms, hindered by limited technological absorption capacity, path dependence, and factor bottlenecks. This study provides empirical support and actionable insights for optimizing land resource allocation and advancing coordinated development between NQP and LUE in similar urban agglomerations. Full article

Green finance is increasingly expected to support decarbonization while enhancing productivity, yet evidence on whether green credit bonds raise green total factor productivity (GTFP) remains limited. Using panel data for 29 provincial-level regions in China from 2016 to 2023, we compute GTFP using a slacks-based measure Malmquist–Luenberger (SBM–ML) index and estimate two-way fixed-effects models. To address endogeneity, we employ a Bartik shift–share instrumental-variable strategy. We found that green credit bonds significantly increase GTFP, with gains driven mainly by technological change (TC) rather than efficiency change (EC). The effect is stronger in eastern and western regions, in provinces that are not low-carbon pilot areas, and in regions with stronger low-carbon governance orientation. Public environmental attention directly improves GTFP but dampens the marginal effect of green credit bonds. Mechanism analyses further indicate that the low-carbon transition of the energy mix (LCEM) is an important transmission channel. Overall, these findings suggest that scaling up and better targeting green credit bonds, alongside complementary governance and public scrutiny, can accelerate China’s transition toward higher green productivity. This provides sustainability-relevant evidence that market-based green finance can support decarbonization while sustaining productivity growth, contributing to long-term sustainable development. Full article

With increasing emphasis on ecological conservation and food security, cultivated land issues have become more prominent. This study focuses on Jilin Province and uses nine prefecture-level administrative units and prefectures as the basic analytical units. Using continuous data for 2000–2023, this study analyzes the spatiotemporal evolution of cultivated land use efficiency (CLUE). By 2023, most regions had achieved ecological safety (ES), examined through their coupling and coordination. The Super-Efficiency SBM-DEA model and the Malmquist–Luenberger (ML) index were used to evaluate the static and dynamic changes in CLUE. A DPSIR–PLS-SEM integrated framework was applied to identify causal mechanisms influencing ES, while the TOPSIS method was employed to assess overall evolutionary trends. In addition, the coupling coordination degree (CCD) model combined with kernel density estimation (KDE) was used to characterize the interaction between CLUE and ES and their spatial evolution. Results indicated the following: (1) From 2000 to 2023, overall CLUE in Jilin Province showed an upward trend with fluctuations, while regional disparities narrowed and spatial distribution became more balanced. (2) The composite ES index increased from 0.3009 to 0.7900, accompanied by a marked expansion of areas classified as secure. (3) The CCD improved from a basic level to a high-quality coordination level, indicating enhanced synergistic development. Higher coordination was observed in central and eastern regions, whereas western and peripheral areas lagged. This study integrates multi-dimensional modeling approaches to systematically assess the coupled dynamics on cultivated land use efficiency and ecological safety, providing insights for land management and policy formulation. Full article

The global shift towards sustainable energy and stringent climate policies has underscored the need for decarbonizing energy systems, electrifying transportation, and transforming supply chains. In this context, China’s new energy vehicle (NEV) industry, as the largest global producer and consumer of automobiles, is pivotal in advancing energy substitution and achieving carbon reduction goals. This study investigates the energy efficiency and supply chain transformation within China’s NEV sector, leveraging panel data from 12 representative provinces over the period 2017–2023. Employing a robust analytical framework that integrates the DEA-BCC model, Malmquist index, and Tobit regression, the study provides a dynamic and regionally differentiated assessment of NEV industry efficiency. The results reveal significant improvements in total factor energy efficiency, predominantly driven by technological progress. R&D intensity, infrastructure development, and environmental regulation are identified as key enablers of efficiency, while excessive government intervention tends to hinder performance. The findings offer valuable empirical insights and policy recommendations for optimizing China’s NEV industry in the context of energy system transformation and sustainable industrial development. Full article

Against the backdrop of accelerating global energy transition, China, as the world’s largest energy producer and consumer, has a crucial impact on achieving carbon neutrality goals through the green development of its power industry. Green total factor productivity is an important indicator for measuring the green development of the power industry. Utilizing provincial panel data from 30 regions in China covering the period 2012–2023, using MATLAB R2021a software, this study firstly measures the static GTFP of China’s power industry using a Super-Efficiency Slack-Based Measure (SBM) model incorporating undesirable outputs. Subsequently, the dynamic GTFP is measured and analyzed using the Global Malmquist–Luenberger (GML) index model. The model decomposes GTFP change to examine the contributions of technical efficiency change and technological progress. The findings reveal that (1) the static GTFP of China’s provincial power industry is generally low, with significant regional disparities, with Jiangsu, Yunnan, Beijing, Zhejiang and Sichuan ranking among the top five nationally; (2) the average GTFPs in eastern and western China are higher than in the central region. Overall, the GTFP of China’s power industry exhibits an upward trend, which is primarily driven by technological progress. Based on these conclusions, the study proposes policy recommendations to enhance the power industry’s GTFP, which can offer theoretical insights for facilitating its green transition and sustainable development. Full article

Renewable energy efficiency (REE) remains critically low across many Sub-Saharan African (SSA) countries, yet the existing literature provides limited empirical clarity on how governance quality shapes efficiency outcomes and through which mechanisms these effects operate. This study addresses this gap by examining the influence of governance quality on REE in 23 SSA countries from 2005 to 2023, drawing on institutional theory and innovation diffusion theory. The analysis investigates three mediating channels, renewable investment, green policy, and green technology, using a multidimensional empirical framework that integrates the Malmquist Productivity Index (MPI), Two-Step System GMM, Generalized Estimating Equations (GEE), Generalized Least Squares (GLS), and Panel-Corrected Standard Errors (PCSE). Results consistently show that governance quality significantly enhances REE through investment, policy, and technological pathways. To capture nonlinearities and heterogeneous responses often overlooked in traditional models, we complement the econometric estimations with causal machine-learning simulations (Double Machine Learning and Causal Forests). These counterfactual analyses reveal that governance improvements and renewable-policy adoption produce the highest efficiency gains in mid-governance countries with stronger absorptive capacity. While the study offers policy-relevant insights, limitations remain, due to data constraints, unobserved institutional dynamics, and the uneven maturity of green-technology systems across the region. Nevertheless, the findings underscore that strengthening governance and fostering innovation are fundamental to accelerating a sustainable and inclusive green-energy transition in Sub-Saharan Africa. Full article

The dynamic relationship between foreign direct investment (FDI) and sustainable development has become a central topic of inquiry for academics and policymakers with rapid global economic growth. This study aims to clarify the impact mechanism and regional heterogeneity of FDI on total factor productivity (TFP) of Chinese high-tech enterprises, providing empirical evidence for optimizing foreign investment policies and promoting sustainable growth of enterprises. We utilized panel data from 30 provinces in China from 2009 to 2022. The DEA-Malmquist index method is firstly employed to dynamically measure the TFP of high-tech enterprises, while a static panel model is utilized to empirically test the impact of FDI on TFP. A particular emphasis is then placed on analyzing the regional heterogeneity of technology spillovers. The findings reveal that FDI significantly enhances both the production efficiency and the technological innovation capacity of high-tech enterprises overall, thereby facilitating the sustainable growth of enterprises. Furthermore, technological innovation emerges as the core driving force behind TFP growth, whereas the expansion of labor input significantly decreases efficiency improvements. Notably, the technology spillover effects of FDI illustrate significant heterogeneity across different regions and types of enterprises. To promote the sustainable development of high-tech enterprises, this study provides evidence-based insights for foreign direct investment technologies to better enhancing the overall sustainable competitiveness of the economy in China. Full article

To solve the spatial water resources shortage, lots of water diversion projects have been constructed for sustaining development. As the water resource utilization efficiency (WRUE) is assumed not to decrease after the operation of water diversion projects, it is necessary to analyze the WRUE and its driving factors in a water-receiving area. Taking the Tao River Diversion Project as a case study, a Super-SBM (Super Slack-Based Measure) model and the Malmquist–Luenberger index are applied in estimating the WRUE values in the seven counties or districts in the water-receiving area of the Tao River Diversion Project. Spatial autocorrelation and a geographical detector are applied to explore the patterns and influencing factors. The results show that there is significant spatial variation in WRUE across the water-receiving areas from 2010 to 2019. High-efficiency areas maintain or improve their efficiencies, while low-efficiency areas show a stagnant or declining trend. The nondecreasing premise of WRUE is not fully satisfied in any area and at any time. The water diversion project is found to be a key driver for the shifting spatial patterns of WRUE from a cold spot dominance to a stronger hot spot agglomeration. The influencing factors on WRUE’s spatial differentiation are also dynamic with the operation of the water diversion project. Therefore, our study will not only help to assess the benefits of the Tao River Diversion Project, but can also provide many valuable insights for water resource planning. Full article

An in-depth and comprehensive evaluation of carbon emission efficiency (CEE) is essential for promoting high-quality development and achieving the “dual-carbon” goals. This study applies a super-efficiency slacks-based measure (Super-SBM) model with carbon emissions treated as an undesirable output to measure provincial CEE and the Malmquist–Luenberger (ML) index across 30 provinces and major comprehensive economic zones in China from 2010 to 2023. Efficiency trends for 2024–2025 are projected using a hybrid Autoregressive Integrated Moving Average (ARIMA)–Long Short-Term Memory (LSTM) approach. Furthermore, CEE patterns are examined at both national and regional levels, and the relationships between CEE and potential drivers are analyzed using Tobit regressions. Combining the regression outcomes with short-term forecasts, this study provides a forward-looking perspective on the evolution of CEE and its associated factors. The results indicate that (1) China’s CEE demonstrates a generally fluctuating upward trajectory, with the southern coastal and eastern coastal regions maintaining the highest efficiency levels, while other regions remain relatively lower. (2) The temporal changes in CEE across economic zones correspond to variations in technical efficiency and technological progress, with the latter contributing more prominently to overall improvement. (3) CEE shows significant associations with multiple factors: population density, economic development, technological advancement, government intervention, and environmental regulation are positively associated with efficiency, whereas urbanization tends to correlate negatively. Based on these findings, policy implications are discussed to promote differentiated pathways for enhancing CEE across China’s regions. Full article

The digital economy (DE) has become an essential driver of sustainable growth under China’s “Dual Carbon” goals of carbon peaking and neutrality. However, limited evidence exists on the DE’s city-level effects on green and low-carbon transition. This study investigates the impact and mechanisms through which digital economy transition (DET) influences urban low-carbon development, utilizing panel data from 283 Chinese cities between 2011 and 2018. A comprehensive digital economy development (DED) index is constructed to measure regional digitalization levels. The findings reveal the following: (1) DET significantly improves CEE, and a one-standard-deviation increase in DED raises CEE by approximately 3.7%. (2) The effect of DET on CEE exhibits regional and resource-based heterogeneity, with western regions and resource-dependent cities benefiting more substantially. (3) The mechanisms through which DET improves CEE include stimulating the technological innovation level, attracting foreign direct investment (FDI), and promoting the financial development level. These insights provide valuable theoretical and practical implications for policymakers seeking to harness the digital economy to achieve sustainable urban development and carbon neutrality. Full article

To evaluate the effectiveness of green finance, this study treats China’s green finance reform and innovation pilot zones as a quasi-natural experiment to assess their impact on urban energy efficiency. This research utilizes a panel dataset of 282 Chinese prefecture-level cities from 2010 to 2023 and employs a multi-period difference-in-differences (DID) model. The core dependent variable, urban green total factor energy efficiency (UGTFEE), is quantified using a non-radial Slack-Based Measure (SBM) efficiency model combined with the Malmquist-Luenberger index. The empirical findings reveal four key points. First, the green finance pilot zones significantly enhance UGTFEE, with policy-affected cities demonstrating an average improvement of approximately 2.0% relative to non-pilot cities. Second, this positive impact is transmitted through two primary mechanisms: the advancement of green technology research and development and the deepening of financial market development. Third, the policy’s effectiveness is heterogeneous, varying according to regional characteristics such as geographical location, environmental regulation stringency, and resource endowments. Finally, a negative spatial spillover is identified, wherein the policy creates a siphoning effect that competitively suppresses the UGTFEE of neighboring cities. These findings provide critical theoretical insights and empirical evidence for optimizing green finance initiatives, thereby facilitating urban industrial transformation toward greater green energy efficiency. Full article