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Evaluating the sustainable development level and obstacle factors of small towns is an important guarantee for implementing China's new-type urbanization and rural revitalization strategies, and is also a key path to promoting the United Nations Sustainable Development Goal 11 (SDG11). Traditional evaluation methods (such as Analytic Hierarchy Process, AHP, and Technique for Order Preference by Similarity to Ideal Solution, TOPSIS) mainly calculate the comprehensive score of each indicator through weighting. These methods have limitations in handling multidimensional data and system nonlinearity, and they cannot fully reveal the complex relationships and interactions within the sustainability systems of small towns. In contrast, the evaluation model combining Principal Component Analysis (PCA) and Catastrophe Progression Method (CPM) used in this study can better handle multidimensional data and system nonlinear relationships, reducing subjectivity in evaluation and improving the accuracy and reliability of the assessment results. The specific research process is as follows: First, based on the United Nations SDG11 framework, using multi-source big data, a theoretical framework and evaluation index system for the sustainable development of small towns suitable for the Chinese context were established. The impact of county-level factors on the sustainable development of small towns was also considered, and an entropy weight-grey correlation model was used to measure these impacts, resulting in a town-level dataset incorporating county-level influences. Secondly, the sustainability levels of 782 top small towns in China were evaluated using the comprehensive evaluation model based on PCA-CPM Model. Finally, an improved diagnostic model was used to identify obstacles influencing the sustainable development of small towns. The main findings include: 52.69% of the small towns have a sustainable development score exceeding 0.7255, indicating that the overall performance of small towns is at a medium to high development level. The development of small towns exhibits significant differences across regions and types, which are closely linked to county-level effects. Economic and social factors are the main obstacles to the sustainable development of small towns, and the impact of these obstacles intensifies from the eastern to the central, western, and northeastern regions. This study provides valuable insights for policymakers and scholars, promoting a deeper understanding of the sustainable development of small towns., Competing Interests: Declaration of competing interest The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Green energy transition directly contributes to Sustainable Development Goals 7 and 13, which are among the 17 development goals determined by the United Nations. However, what are the determinants of this green energy transition? Researchers' answers to this question will assist in formulating policy prescriptions that will enable concrete steps toward achieving these goals. This study investigates how the development of the banking sector, human rights, economic complexity, and economic freedom affect the green energy transition in China, which is the biggest emitter of greenhouse gases globally but is also one of the leading countries in renewable energy production. The study covers the years 1995-2022. Advanced time series analysis methods were employed, and robust results were reported. According to the findings, Economic Freedom, Human Rights, and Economic Complexity have increased the transition to green energy in China. No effect of banking sector development was found. The country should improve economic freedom and human rights and increase knowledge/innovation-based production to accelerate China's transition to green energy and contribute to environmental sustainability., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The distribution of China's energy resources is concentrated in the central and western regions, whereas the energy demand is predominantly centered in the eastern regions. Under the ambitious "carbon neutrality" goal, how to effectively quantify and respond to the impact of this energy "endowment-demand" distortion (EEDD) on economy and environment has become a key issue. We introduce an EEDD index that quantifies the distortion characteristics of China's energy endowment and demand. Based on the EEDD index, a panel vector auto regression-generalized method of moments (PVAR-GMM) model is adopted to assess the interactive effects between regional EEDD and sustainable development variables. The obtained results indicate that the energy endowment-demand distortions are progressively worsening across most provinces. Interestingly, we discover that the EEDD has significant beneficial effects on regional sustainable development. Moreover, the EEDD is found to be significantly influenced by the sustainability-related variables. These impacts between EEDD and sustainable development variables demonstrate significant regional heterogeneity. This study provides crucial empirical evidence for advancing the comprehensive and sustainable development of regional energy, environment, and economy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Achieving sustainable development goals (SDGs) has garnered significant attention from academia and policymakers worldwide. In this study, we examine the impact of ICT, technological innovation (TI), and environmental policy stringency (EPS) on SDI, considering the moderating role of governance quality (GQI) and transport infrastructure (TIS). A comprehensive dataset of 17 advanced nations is utilized from 1996 to 2021. To capture the dynamic and extreme marginal impacts of these policy instruments on SDG attainment, we employ the advanced technique of Feasible Generalized Least Square (FGLS). The results demonstrate that ICT has a positive and significant effect on SDGs, particularly when combined with high levels of governance quality (GOV) and transport infrastructure (TIS). Likewise, TI has a positive impact on SDGs, especially in the presence of strong governance. Furthermore, EPS exhibits a positive association with SDGs. The findings also reveal that while governance hurts SDGs, this effect diminishes when combined with higher levels of ICT, TI, and EPS, and when TIS positively moderates the relationships. The robustness estimations using DOLS and PCSE methods validate the FGLS findings. These results underscore the importance of ICT, TI, and EPS in advancing sustainable development. Moreover, they highlight the significance of good governance and robust transport infrastructure in maximizing the positive effects of these factors. These findings hold implications for policymakers and stakeholders involved in promoting sustainable development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Digitalization, as a crucial engine for promoting sustainable development, has created new prospects for enterprise green transformation. Utilizing panel data from enterprises registered in China's resource-based cities from 2010 to 2021, this study innovatively examines the role of digitalization in promoting green transformation across two key performance dimensions. The findings indicate that enterprise digitalization significantly enhances the green innovation performance, while its impact on environmental performance, although positive, is not statistically significant. Overall, enterprise digitalization greatly facilitates green transformation. Moreover, mechanism analysis reveals that enterprise digitalization promotes green transformation through the alleviation of financial mismatch and the increase of external market attention. In addition, heterogeneity analysis shows that the effectiveness of enterprise digitalization in promoting green transformation is significantly different in different opportunist risk scenarios, proving more effective in areas with low-marketization, high-competition industries, and high environmental regulation intensity. In terms of the policy level, the National Big Data Comprehensive Pilot Zone (NBDCPZ) and the Carbon Emission Trading Policy (CETP) played a positive stimulating role in the process of enterprise digitalization affecting green transformation. This paper broadens the scope of research on green transformation, offers new development philosophy for enterprises in resource-based cities, and provides new directions for the synergistic development of digitalization and greenization., Competing Interests: Declaration of competing interest No conflict of interest exits in the submission of this manuscript, and the manuscript is approved by all authors for publication., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Sustainable development addresses global challenges by promoting practices that balance economic, social, and environmental considerations. Key factors include the shifting to green energy and the integrating of green technology in the sustainable development process. This study investigates the heterogenous effects of green technology development, green energy, R&D expenditures, FDI, economic growth, and urbanization on CO 2 emissions in 25 European Union (EU) countries using panel quantile regression over the period 2000-2021. The results, based on panel quantile regression, indicate that green energy decreases CO 2 emissions from the 10th to the 90th quantiles, while green technology development increases CO 2 emissions at the lower quantiles (10th to 60th) and then turns negative. The robustness of the fixed effect model also confirms the findings of the study. Additionally, panel causality tests indicate no causal link between green technology development and CO 2 emissions, but there is bidirectional causality between green energy and CO 2 emissions. Therefore, the findings highlight that policymakers should thoroughly evaluate measures and strategies to encourage the development of green technologies and green energy sources to reduce high levels of CO 2 emissions. One strategy is to provide financial aid and support technological advances to produce green energy at reduced costs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Resource-based cities (RBCs) worldwide with a single industrial structure face the double pressures of sustainable development to promote development (i.e., industrial upgrading) and mitigating carbon emissions. Although building extraregional linkages is a potential path to advance this goal, the action of these linkages still requires study since there are many contradictory conclusions in the literature. To fill this gap, the study addresses the relationship between extraregional linkages, industrial upgrading, and the low-carbon transition in RBCs from 2012 to 2019 with the help of econometric panel models with proposed variables (e.g., the coupling coordination degree of extraregional technology and investment, CCD) built from multiple new data sources. The results are as follows. First, the diversification and specialization of the local industrial structure in RBCs both reduce carbon efficiency (CE). Second, extraregional technology, on its own, does not directly enhance CE as investments do. Third, the CCD not only serves to augment CE but also acts as a mitigating factor against CE reduction during industrial diversification. Based on the above findings, distinct low-carbon transition pathways are suggested for various types of RBCs, considering their positions within the extraregional linkage network., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

We developed a high-resolution machine learning based surrogate model to identify a robust land-use future for Australia which meets multiple UN Sustainable Development Goals. We compared machine learning models with different architectures to pick the best performing model considering the data type, accuracy metrics, ability to handle uncertainty and computational overhead requirement. The surrogate model, called ML-LUTO Spatial, was trained on the Land-Use Trade-Offs (version 1.0) model of Australian agricultural land system sustainability. Using the surrogate model, we generated projections of land-use futures at 1.1 km resolution with 95% classification accuracy, and which far surpassed the computational benchmarks of the original model. This efficiency enabled the generation of numerous SDG-compliant (SDGs 2, 6, 7, 13, 15) future land-use maps on a standard laptop, a task previously dependent upon high-performance computing clusters. Combining these projections, we derived a single, robust land-use future and quantified the uncertainty. Our findings indicate that while agricultural land-use remains dominant in all Australian regions, extensive carbon plantings were identified in Queensland and environmental plantings played a role across the study area, reflecting a growing urgency for offsetting greenhouse gas emissions and the restoration of ecosystems to support biodiversity across Australia to meet the 2050 Sustainable Development Goals., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The increasing demand for economic development in the coastal zone is exerting greater pressure on the eco-environment in this region. Therefore, it is imperative to conduct comprehensive research on the sustainable development of the eco-environment in coastal areas. Grounded in sustainable development theory and Integrated Coastal Zone Management (ICZM), this study assessed sustainable development of Jiaozhou Bay (JZB), China over the period from 2015 to 2019 under multiple pressures applying the Driver-Pressure-State-Impact-Response (DPSIR) framework. Subsequently, a SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis was employed to propose specific countermeasures and recommendations. In terms of weight within the criterion layer, Response (R) and Pressure (P) were assigned higher weights. The comprehensive assessment value Z in the target layer indicated a slight decrease in 2016 followed by a gradual increase that eventually led to stabilization. The comprehensive assessment values of the indicators varied depending on different conditions, implying that higher values would contribute to sustainable development of the eco-environment. Based on a SWOT analysis, strategies for Qingdao were derived from a combination of research findings and future development policy. These strategies include preventing and controlling land and sea pollution, governing reclamation activities, optimizing industrial structure, and strengthening public opinion guidance. This study highlights the application of quantitative and qualitative analysis methods in ICZM, providing specific recommendations based on policy analysis for achieving sustainable development in JZB's eco-environment under multiple pressures., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

As the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. in order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. through the use of Support Vector Machine (SVM) Multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. with wind power base a as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terAs the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. In order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. Through the use of Support Vector Machine (SVM) multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. With Wind Power Base A as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terms of the number of units, internal rate of return within the project, and annual average equivalent utilization hours at Wind Power Base A. The number of units decreases to 5860, with total purchase and transportation costs remaining basically unchanged, the internal rate of return increases to 9.3%, and annual equivalent utilization hours increase to 2044 h. Energy consumption and CO2 emissions are significantly reduced, with energy consumption decreasing by 0.68 × 109 kgce and CO2 emissions decreasing by 1.29 × 109 kg. The optimization effects are mainly concentrated in the production and installation stages, with emission reductions achieved through the recycling and disposal of materials consumed in the early stages. In terms of investment benefits, environmental benefits are enhanced, with a 13.93% reduction in CO2 emissions. Moreover, there is improved energy efficiency, with the energy input-output ratio increasing from 7.73 to 9.31. This indicates that the Wind Power Base A project has significant environmental and energy efficiency advantages in the clean energy industry. This work innovatively provides a comprehensive assessment and optimization scheme for clean energy projects and predicts the profitability of Wind Power Base A using SVM multi-factor models. Besides, this work optimizes key parameters of the project using a bi-level multi-objective approach, thus comprehensively improving the investment return and utilization efficiency of the wind power base. This work provides innovative methods and strong data support for the development of the clean energy industry, which is of great significance for promoting sustainable development under the backdrop of green finance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This paper addresses the need to understand the structure of sustainability change in the face of the unpredictability of modern civilization. The aim of the paper is to examine the structure of sustainability change in relation to smaller spatial units such as the regions of the European Union and to compare these results with those achieved at the country level. For this purpose, the sustainable development indicators available in the Eurostat database were analyzed. Studies published to date have tended to focus on the performance of national economies or selected regions. In this article, the available data on regions are also analyzed. The study aims to compare the level of sustainable development achieved at the level of EU countries and regions located within them. Methodologically, the study uses advanced methods of comparative analysis, and the synthesis of the data enables the construction of a synthetic measure using taxonomic methods, facilitating the classification of EU countries and regions into clusters. The results highlight significant divergences in sustainability achievements between EU countries and regions. The study reveals the importance of studying sustainable development in smaller geographical cross-sections. The results highlight the need to expand access to data at the regional level (sustainability indicators) to facilitate more comprehensive analyses and inform targeted policy interventions. Looking to the future, the study recommends further research into the inter-linkages between sustainability and other critical areas in EU regions, such as innovation and competitiveness., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Countries' circularity performance and CO 2 emissions should be addressed as a part of the UN net-zero Sustainable Development Goals (SDGs) 2030. Macro-scale circularity assessment is regarded as a helpful tool for tracking and adjusting nations' progress toward the sustainable Circular Economy (CE) and SDGs. However, practical frameworks are required to address the shortage of real-world circularity assessments at the macro level. The establishment of CE benchmarks is also essential to enhance circularity in less sustainable nations. Further, monitoring the extent to which nations' circularity activities are sustainable and in line with the SDGs is an area that lacks sufficient practical research. The current research aims to develop a macro-level framework and benchmarks for national sustainable circularity assessments. Methodologically, we develop a dynamic network data envelopment analysis (DN-DEA) framework for multi-period circularity and eco-efficiency assessment of OECD countries. To do so, we incorporate dual-role and bidirectional carryovers in our macro-scale framework. From a managerial perspective, we conduct a novel comparative analysis of the circularity and eco-efficiency of the nations to monitor macro-scale sustainable CE trends. Research results reveal a significant performance disparity in circularity, eco-efficiency, and benchmarking patterns. Accordingly, circularly efficient nations cannot necessarily be considered eco-friendly and sustainable. Although Germany (as a superior circular nation) can be regarded as a circularity benchmark, it cannot serve as an eco-efficiency benchmark for less eco-efficient nations. Hence, the new method allows decision-makers not only to identify the nations' circularity outcome but also to distinguish sustainable nations from less sustainable ones. This, on the one hand, provides policymakers with a multi-faceted sustainability analysis, beyond the previous unidimensional analysis. On the other, it proposes improvement benchmarks for planning and regulating nations' future circularity in line with real sustainability goals. The capabilities of our innovative approach are demonstrated in the case study., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

China's double carbon target aims to improve human well-being and sustainable development. Energy transformation welfare performance (ETWP) is the efficiency of energy transition (ET) in enhancing human well-being. ETWP considers both human well-being and sustainable development. Research on its driving force is helpful in achieving the double carbon goal. Thus, this paper used Logarithmic Mean Divisia Index Model, Fixed Panel Regression Model and Grey Relational Analysis Model to analyze China's ETWP from 2006 to 2022 and predicted ETWP of 31 provinces from 2023 to 2030. The results showed that: (1) ETWP had two rising periods in 2006-2014 and 2015-2022. (2) The government's rationalization policy on energy and environment and technological innovation ability were fundamental driving forces for improving ETWP. (3) There were obvious spatial and temporal distinctions in ETWP, and it would bring out different degrees in most areas. Thus, ET should be promoted by improving the ecological environment and resource utilization efficiency; The importance of the role of scientific and technological innovation and policies should be focused on in promoting ETWP; ET policies based on local developments should be formulated and the energy structure should be changed., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In order to deal with the environmental problems such as pollution emissions and climate change, sustainable development in the field of transportation has gradually become a hot topic to all sectors of society. In addition, promoting the green and low-carbon transformation of China's transportation is also an important issue in the new era. Thus, it is particularly important to correctly identify the green effect of high-speed rail. However, the traditional causal reasoning model faces several challenges such as 'dimensional curse' and multicollinearity. Based on the panel data of 283 prefecture-level cities in China from 2003 to 2019, this study uses the double machine learning model to explore the impact of transportation infrastructure upgrading on the efficiency of urban green development in China. The research shows that the upgrading of transportation infrastructure can effectively improve the efficiency of urban green development by 4%. Service industry agglomeration and green innovation are verified as two mediating channels. Moreover, the synthetic difference in difference model is employed to evaluate the regional impact of high-speed rail, and finds that the regional impact of transportation policies often exceeds the impact of individual cities. We further apply the conclusions of this paper to the research at the micro enterprise level. Goodman-Bacon decomposition and a variety of robustness tests confirm the validity of our conclusions. The study's comprehensive empirical analysis not only validates the positive effects of transportation upgrades on green development, but also offers novel insights into the underlying mechanisms and policy implications of transportation upgrading., Competing Interests: Declaration of competing interest No conflict of interest exits in the submission of this manuscript, and the manuscript is approved by all authors for publication., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Japan is progressing towards its circular economy (CE) goals as many of its cities have adopted circular city (CC) policies and programs, although further progress is constrained as a result of the lack of a common framework. A novel framework was proposed with the "European circular cities declaration" (ECCD) (2020), consisting of a list of 10 principles committing to integrate circularity into the city's design, development, and management. As a foremost finding, and building on the authors' previous studies of Japan's CE and CC, this work shaped a circular cities declaration (CCD) for Japan following a triple-axis methodology; It (1) evaluates the ECCD as a baseline, (2) adapts it to Japan's unique socio-economic landscape, and (3) considers the three pillars of sustainable development, offering practical guidance for governments facing similar challenges. This environmental management tool goes beyond the EU one providing a model of hybrid governance and monitoring and evaluation mechanism. The resulting declaration is intended for the government to facilitate a transition from insulated CE policies to holistic CC ones, but also for businesses, academia, and communities; Thus, it may aid in endorsing a cities' common framework and shared vision to harness the potential of CC to address environmental issues, foster innovation and collaboration toward a resilient future in Japan., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The circular economy (CE) has been identified as a critical strategy to contribute to sustainable development and the achievement of the Sustainable Development Goals of the 2030 Agenda, being one of the biggest challenges for society in general and organisations in particular. Hence, various methods have been developed in recent years to measure the level of circularity of organisations, such as methodologies, indicators, metrics and CE tools. Universities, as education, research and community service organisations, play a key role in the promotion of and transition to the CE. Therefore, they require methods that enable them to quantify and monitor their level of circularity. However, it has been shown that CE methods developed for organisations are not suitable for such complex institutions, and, to date, no sector-specific methods have been developed. In this context, it has become necessary to develop a useful method to quantify and monitor the current level of circularity, assess its progress and facilitate decision-making on circularity that addresses the specific needs of this type of organisation. Thus, a set of 82 CE indicators and their metrics, specific to universities, has been proposed. They have been implemented in an Excel spreadsheet tool called CExUNV, in order to promote and facilitate their use. In addition, 41 improvement actions have been suggested to guide and assist universities in their progress towards CE., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Drylands, as highly vulnerable ecosystems, support environmental functions and human well-being. Nevertheless, widespread land degradation and desertification present significant global and regional environmental challenges, with limited consensus on their area and degree. This study used time-series vegetation productivity and meteorological data from 2000 to 2020 to quantify global land degradation trends and driving factors in drylands. The results show a notable restoration of land degradation in drylands worldwide, with the area of improved land exceeding the degraded area by 1.4 times, although the threat of degradation persists. India and China emerge as pioneers in effective land improvement strategies, offering valuable experiences for other regions. Combined effects, as quantitatively distinguished by our established model, dominate the degradation and improvement processes. Notably, human activities play a decisive role in influencing land degradation trends, with the potential for either exacerbation or reversal. This study provides new perspectives on environmental health and human activities from global and regional observations. Finally, our research provides scientific support for desertification control and contributes to the overall advancement of the SDGs globally., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Urban greenness serves as a key indicator of sustainable urban development, with smart city construction emerging as a primary strategy for its enhancement. However, there is little empirical evidence considering multi-dimension between urban greenness and smart city construction on the city level. This study focuses on the impact on urban greenness of smart city construction in megacities, using the difference-in-differences regression model to evaluate the impact based on urban development conditions in various aspects from 2010 to 2021 in 10 megacities in China. The results of panel data of different indicator samples show unique conclusions. First, smart city pilot policy in megacities has significant impact on urban greenness, primarily due to demographic and economic developments. Second, the impact is different between the megacity and national level, and different factors of urban greenness have different effects on smart city construction. Third, the effects are time-lagged and lasted for years, and regional heterogeneity divided by building climate zones is existed, where the effect is more obvious in city agglomeration. These findings of smart city construction reveal the unique influences on megacity greenness, and can be generalized to cities with similar characteristics accordingly., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Changing humanity's relationship with the ocean is identified as one of ten key challenges in the UN Decade of Ocean Science for Sustainable Development (2021-2030). Marine citizenship is one potential policy approach for reducing anthropogenic harms to the ocean and promoting ocean recovery, and there is a need to better understand marine citizenship motivating factors and their interactions. To contribute to a more holistic understanding, we approached this problem using an interdisciplinary, mixed methodology, which prioritised the voices and experiences of active marine citizens. An online survey and semi-structured interviews were conducted to examine factors spanning environmental psychology (values, environmental identity) and human geography (place attachment and dependency). Our data uncovered a unique marine place attachment, or thalassophilia, which is a novel conceptualisation of the human capacity to bond with a type of place beyond human settlements or defined localities. It is the product of strong emotional responses to the sensorial experience of the ocean and shared social or cultural understanding of ocean place identifications. A key driver of deeper marine citizenship is marine place dependency, and it is positively influence by stimulation and non-conformity values, environmental identity, and thalassophilia. We map significant motivating factors to identity process theory and describe a novel marine identity concept. We propose this as an operational mechanism of marine citizenship action, potentially filling the value- and knowledge-action gaps in the context of marine environmental action. This research provides a cornerstone in marine citizenship research by analysing together in one study a multitude of variables, which cross human-ocean relationships and experiences. The identification and characterisation of thalassophilia and marine identity process theory will enable research and practice to move forwards with a clearer framework of the role of the ocean as a place in environmental action., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Financial development and geopolitical risks can significantly affect sustainable development. However, the roles of these factors in sustainable development are rarely investigated. Thus, this study takes into account the role of geopolitical risk while exploring the effects of financial development, natural resource rents, and eco-innovation on sustainable development in the Organization for Economic Co-operation and Development (OECD) countries. To this end, yearly data from 1990 to 2019 is analyzed using advanced econometric tests. The Common Correlated Effects Mean Group (CCEMG) results indicate that financial development and eco-innovation are significantly and positively related to sustainable development. Natural resource rents have a detrimental impact on sustainable development which confirms the presence of the resource curse hypothesis in OECD countries. Furthermore, the results revealed that controlling geopolitical risk is useful in fostering sustainable development. Lastly, the panel Granger causality test unveiled one-way causality from financial development, eco-innovation, natural resource rents, and geopolitical risk to sustainable development. Moreover, causalities are found from geopolitical risk to financial development, eco-innovation and natural resources. These findings suggest that OECD countries should prioritize financial development and eco-innovation policies for sustainable development while mitigating the negative effects of natural resource rents. The geopolitical risk can harm sustainable development, so policymakers should promote international cooperation and risk-sharing., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The 2030 agenda of the United Nations provides a framework of 17 Sustainable Development Goals (SDGs) and 232 indicators for its members to fulfill. The overall achievement critically depends on how nations understand the interactions between these SDGs and set priorities for development pathways. This study provides a comprehensive network analysis of global SDG complementarities, measured by the co-occurrences of SDG pairs' comparative advantages in the same region by adopting the 'product space' concept from economics. We construct the 'SDG space' at goal and indicator levels with the most recently available data and then validate its robustness by comparing it to the commonly used correlation network and confirm its predictive power using historical data. Network analysis reveals a strongly connected socioeconomic-related core and an environmental-related periphery, with 'bridge' indicators connecting different clusters. The goal-level space identifies the 'bridge' goals as SDG 17 (Partnerships for the Goals), SDG 8 (Decent Work and Economic Growth), and SDG 15 (Life on Hand) in the environmental-related cluster, while identifying SDG 7 (Affordable and Clean Energy), SDG 6 (Clean water and Sanitation), and SDG 16 (Justice and Strong Institutions) in the socioeconomic cluster. The indicator-level space provides details to explain how they act as 'bridges' in the network. In particular, 16-9: Free Press Index is the 'bridge' indicator with the highest betweenness centrality value and acts as the bottleneck indicator in China for its overall sustainable development. Improving it can enhance connected indicators' performance, leading to positive cascading effects on different aspects of sustainability., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Anaerobic Digestion (AD) technology emerges as a viable solution for managing municipal organic waste, offering pollution reduction and the generation of biogas and fertilisers. This study reviews the research works for the advancements in AD implementation to effectively impact the UN Sustainable Development Goals (SDGs). Furthermore, the study critically analyses responsible waste management that contributes to health and safety, elevating quality of life in both rural and urban areas and, finally, creates a map of AD outputs onto all 17 SDGs. Finally, the assessment employs the three sustainability pillars (i.e., economic, environmental, and social perspectives) to examine the direct and indirect links between AD and all 17 UN SDGs. The findings reveal substantial progress, such as poverty reduction through job creation, bolstering economic growth (SDGs 1, 8, 10, 12), enhancing agricultural productivity (SDG 2), advancing renewable energy usage and diminishing reliance on fossil fuels (SDG 7), fostering inclusive education and gender equality (SDGs 4, 5, 9), combating climate change (SDG 13), transforming cities into sustainable and harmonious environments (SDGs 11, 16, 17), and curbing environmental pollution (SDGs 3, 6, 12, 14, 15). Nonetheless, the study highlights the need for further efforts to achieve the SDG targets, particularly in part of liquid and solid fertilisers as the AD outputs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Achieving sustainable economic development and mitigating climate change require effective green transformation management. This study builds an evolutionary game model for industrial enterprises, local governments, and the central government, analyzing the dynamic interactions among vertical decentralization, environmental regulation, and enterprise pollution. Our research reveals that increasing environmental taxes can incentivize industrial enterprises to adopt green transformation practices and promote governments at all administrative levels to supervise and enforce environmental regulations. Moreover, in the context of vertical decentralization, financial incentives provided by the central government to local governments become critical drivers for promoting green transformation. Furthermore, the additional social benefits resulting from local government supervision and governance are key factors in green transformation management, while the negative social effects of industrial enterprises not rectifying their actions are noteworthy. Our study emphasizes the need for an integrated framework incorporating these critical elements for successful green transition management. The findings of this research provide valuable insights for developing nations seeking to enhance their governance capacity throughout the green transformation process., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Resource-based cities generally face the dual pressures of ecological damage and high carbon emissions. Taking the National Sustainable Development Plan for Resource-Based Cities (NSDP) issued in China as a quasi-natural experiment, this paper for the first time, uses the difference-in-difference method to test the coordinated effect of green expansion and carbon reduction brought by NSDP at the county level using rich remote sensing data. The results show that NSDP has significantly promoted the ecological quality improvement and CO 2 emission mitigation in resource-based cities, and the above coordinated benefits mainly occur in energy, mature, declining, northeastern, and municipal resource-based cities. Through the mechanism analysis, we find that NSDP mainly works by promoting the transformation of industry sector to agriculture, animal husbandry, accommodation and catering, finance, and environment management sectors, which are reflected in output, employment, product yield, land type and land transaction area. Further discussion on the tertiary industry suggests that developing tourism, easing financing constraint and strengthening environment regulation, are the key channels to borne fruit in green expansion and carbon reduction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Businesses embracing green innovation can encourage high-quality green economic development in addition to reducing emissions. In this paper, we use the Difference-in-Differences (DID) to investigate the influence of green investor behavior on the green innovation of companies, using the first-ever green investor investment in a company as a quasi-natural experiment. According to research, green investors have the power to accelerate corporate green innovation greatly. Three key strategies that green investors can use to do this include raising institutional investment levels, enhancing the green perception of executives, and bringing in top talent. Heterogeneity analysis shows that non-high-polluting, big, and state-owned enterprises (SOEs) are more likely to benefit from green investors' green innovation effects. Further analysis reveals that (ⅰ) green investors' influence on an enterprise's level of green innovation can help it improve its ESG ratings; (ii) green investors can encourage green innovation in source control but have little effect on green innovation in end-of-pipe treatment; (ⅲ) green investors can support both non-green and green innovation in enterprises, but have a greater influence on green innovation. This study strengthens the micro relationship between green investors and corporate green innovation. It also supports the theoretical underpinnings of corporate green innovation, which is significant for advancing green innovation, environmental protection, and high-quality economic development in emerging economies. • This paper explores the relationship between green investor behavior and corporate green innovation. • Green investors have the power to accelerate corporate green innovation greatly. • Green investors significantly accelerate corporate green innovation mainly through raising institutional investment levels, enhancing the green perception of executives, and bringing in top talent. [ABSTRACT FROM AUTHOR]

The development of digital finance provides new opportunities for improving energy efficiency and promoting green development. This paper calculates green total factor energy efficiency (GTFEE) using the super-efficiency SBM model and examines the impact of digital finance on GTFEE. Digital finance has a significant positive impact on GTFEE. Under a bank-dominated financial structure, the positive impact of digital finance on GTFEE is quite significant. In regions with intense banking competition, a large amount of green credit, and lower resource dependence, digital finance is conducive to enhancing GTFEE. Optimizing the allocation efficiency of production factors is an essential mechanism for digital finance to encourage improvements in GTFEE. Digital finance alleviates distortions in factor markets and enhances the matching of the marginal output and the price of capital, labor, and energy factors, thereby facilitating improvements in GTFEE. Further analysis indicates that digital finance has a significant, positive spatial spillover effect on GTFEE, enhancing GTFEE levels in both local and neighboring regions. This study enriches the research on the relationship between digital finance and energy efficiency and provides theoretical foundations and policy references for how digital finance can better serve the green transition of the economy. • This paper analyzes the impact of digital finance on green total factor energy efficiency (GTFEE). • Digital finance alleviates distortions in factor markets, thereby promoting GTFEE. • Digital finance's impact on GTFEE depends on financial structure, banking competition, green credit and resource dependence. • Digital finance has a significant positive spatial spillover effect on GTFEE. [ABSTRACT FROM AUTHOR]

In the context of global low-carbon emission reduction, how to achieve green and high-quality development has become a major issue for the Communist Party of China (CPC) and the Chinese government recently. Based on the data of China's listed companies from 2013 to 2020, this paper uses Python to implement text analysis of annual reports, and explores the relationship between political acuity and carbon-efficiency synergies (CES) from the perspective of enterprise initiative. We found that (1) political acuity positively affects carbon-efficiency synergies. (2) Increased political acuity can reduce carbon emissions, but the effect on economic efficiency is not obvious. That is, low carbon takes the lead in raising the level of carbon-efficiency synergies. (3) Environmental regulations can positively regulate the relationship between political acuity and carbon-efficiency synergies. (4) Political acuity in southern China, carbon neutral and non-state-owned enterprises (NSOEs) will have a more pronounced effect on carbon-efficiency synergies. From the perspective of the root causes of political linkages, we find the synergies between formal and informal institutions, and the key factors for policy implementation. This paper is helpful for enterprises to improve the synergies of emission reduction and efficiency promotion, and has practical implications for the government to promote green and high-quality development. • Assess the value of the low-carbon economy from its political roots. • We build a carbon-efficiency synergy evaluation system. • Political acuity and formal institutions are synergistic. • The influence of political acuity on CES can be replicated. • This paper expands the technical approach to low-carbon research. [ABSTRACT FROM AUTHOR]

Green innovation is important and beneficial for the green development of enterprises. The development of digital technology could improve the asymmetry of information and affect the enterprise's financing modes. Currently, a definitive conclusion about the relationship between the digital transformation and green technology innovation has not reached. Additionally, studies have not yet fully explored the impact pathways of corporate digital transformation on green innovation through corporate financing modes. To fill this void, this study collected data of Chinese A-share listed companies from 2010 to 2020. The empirical analysis results showed that digital transformation significantly enhanced enterprises' green innovation abilities. Furthermore, mediating models were utilized to explore how enterprise financing modes mediate the impact of digital transformation on the enterprise green innovation. Results indicated that through internal financing and bank loans as "bridges", digital transformation promoted enterprise green innovation. However, digital transformation did not have a significant impact on enterprise green innovation through venture capital. Moreover, heterogeneity analyses revealed significant differences among different groups of enterprises. This study expands the research field related to digital transformation and enterprise green innovation by providing empirical evidence to uncover the impact of digital transformation on enterprise green innovation. Findings of this study contribute not only to enterprise managerial decision-makers to better leverage digital transformation through corporate financing to accelerate green innovation, but also to government policymakers to design policies to stimulate enterprise green innovation. • Firstly, our paper empirically examines and validates the impact of digital transformation on enterprise green innovation again by using empirical data of biggest development counties, i.e., China. • Secondly, our paper combines enterprise digital transformation, financing modes, and green innovation within the same framework to study the "bridge" role of financing modes between digital transformation and enterprise green innovation. • Thirdly, Thirdly, heterogeneity analyses were carried out, the results of the different enterprises groups are significant different. [ABSTRACT FROM AUTHOR]

High carbon emissions and population aging are two obstacles to China's development as an emerging economy. It is urgent to scientifically examine the impacts of population aging on carbon emissions to discover new pathways for urban carbon emission reduction. However, existing studies face challenges in terms of focusing on and methodology for accurately capturing the role of industrial production and energy consumption in the environmental effects of population aging. This paper constructs a chain mediation analytical framework and systematically examines the intrinsic linkages between population aging and carbon emissions in Chinese cities from 2000 to 2020 using a two-way fixed effects model. This paper has three main findings. (1) The positive correlation between urban population aging and carbon emissions development is generally low, with coupling coordination degree values of 0.4233, 0.4458, 0.4220, 0.4715, and 0.4665 for each yearly cross-section. (2) For every 1% increase in the population aging rate, carbon emissions decrease by 0.3478% on average. The carbon reduction effect of population aging is significantly greater in low-emissions cities and high-income cities than in other cities. (3) The industrial upgrading (IU) path, energy conservation (EC) path and chain mediation (CM) path account for 72.43%, 7.23% and 20.34%, respectively, of the indirect effects. If the causal link between IU and EC is not considered, the EC effect will be overestimated by 281.16%. The results of the study suggest that properly coping with population aging and reducing carbon emissions are not completely opposed to each other, a conclusion that passes both endogeneity exclusion and robustness check. This paper advocates replacing the one-size-fits-all approach in carbon emissions management and investing more in policy preferences to support carbon emission reduction in both high-emissions and low-income cities. [Display omitted] •The synergistic trends of carbon emissions and population aging are not apparent. •Population aging significantly reduces carbon emissions at the city scale. •Linkage effect of industrial upgrading and energy conservation on carbon emission reduction. •Energy conservation effect of population aging amplified by industrial upgrading. [ABSTRACT FROM AUTHOR]

Enhancing the green economy efficiency (GEE) is crucial for building a sustainable economy. How can the rapidly advancing digital transformation contribute to this process? The paper empirically examines the direct and spatial spillover effects of digital transformation on cities' GEE in China. This study utilizes the National E-commerce Pilot City (NEPC) policy as a quasi-natural experiment of regional digital transformation and employs the staggered difference-in-differences (DID) method with heterogeneous effects. The findings reveal that (i) implementing the NEPC policy significantly increases urban GEE by 2.6%, corresponding to a 16% increase in the mean of GEE. This effect is particularly pronounced in non-resource-based cities and cities with high Internet penetration. (ii) The mechanism test shows that the pilot policy positively affects GEE by promoting green structural transformation, enhancing green innovation, and strengthening public environmental concerns. (iii) The study highlights a positive spatial spillover effect of the NEPC policy on the GEE of nonpilot cities. (iv) The adoption of the NEPC plays a pivotal role in advancing energy use and carbon emission efficiency. This paper expands the existing knowledge on the green development effects of the digital economy while offering valuable policy insights for building an "Inclusive Green Economy". [Display omitted] • The staggered differences-in-differences method is employed to examine the direct and spatial spillover effects of NEPC on cities' GEE in China. • This paper uses NEPC as a quasi-experiment for digital transformation. • NEPC significantly improves green economic efficiency by 2.6%. • Green structural transformation, innovation, and environmental concerns are main channels. • Positive spillover effect of NEPC on green economy efficiency in non-pilot cities. [ABSTRACT FROM AUTHOR]

Higher education institutes (HEIs) are important drivers for the development and implementation of best practices for environmental sustainability. However, reliable indicators are needed to objectively evaluate the environmental performance of HEIs and their policies. The present paper aims at identifying suitable indicators for unbiased comparisons among different HEIs and for the identification of temporal trends in terms of environmental sustainability performance. At this aim, sustainability reports made publicly available by 24 Italian HEIs over a 10-year period were considered. Normalization of sustainability variables such as the annual electrical and thermal energy consumptions, related greenhouse gas emissions, and water consumption, against context-specific factors such as the number of users of each university, latitude, illuminance, heating degree days (HDDs) and cooling degree days allowed identifying the actual possible disturbance of the same variables. HDDs were found to positively affect the thermal energy consumption and the related CO 2 emissions. Based on this, a novel indicator was formulated where the actual value of thermal energy consumption and the related CO 2 emissions are divided not only by the number of users but also by the HDDs of the HEIs' locations. Indeed, this is a remarkable finding that, prior to confirmation with data from world HEIs, could be implemented in world university green ranking systems for improved and less biased sustainability assessments. [Display omitted] • Context-independent indicators allow unbiased sustainability comparison among HEIs. • Context-independent indicators were identified through coefficients of variation. • Per capita thermal energy consumption needs to be divided by heating degree days. • Per capita CO 2 due to thermal energy needs to be divided by heating degree days. [ABSTRACT FROM AUTHOR]

Urban areas contribute 85% of China's CO 2 emissions. Green finance is an important means to support green energy development and achieve the low-carbon transformation of high-energy-consuming industries. The motivation of this article is to investigate the impact and mechanism of green finance on urban carbon intensity. Most existing literature uses linear models to investigate urban carbon intensity, ignoring the nonlinear relationships between economic variables. The nonparametric models can fill the inherent shortcomings of linear models and effectively simulate the nonlinear nexus between economic variables. Based on the 2011–2021 panel data of 237 cities in China, this paper applies the nonparametric additive model to survey the influence of green finance on urban carbon intensity. Empirical findings exhibit that green finance exerts an inverted U-shaped effect on urban carbon intensity, indicating that the carbon reduction effect of green finance has gradually shifted from inconspicuous in the early stages to prominent in the later stages. Then, from the perspectives of region, city size, and carbon intensity, this article conducts heterogeneity analysis. The results show that the impact of green finance on various carbon intensities all exhibits obvious nonlinear feature. Furthermore, this article employs a mediation effect model to conduct mechanism analysis. The results display that technological progress and industrial structure are two important mediating variables, both of which produce an inverted U-shaped nonlinear impact on urban carbon intensity. • The nexus between green finance and urban carbon intensity is a ∩-shaped pattern. • The high-carbon intensity cities receive a U-shaped impact from green finance. • Technological progress exerts a ∩-shaped effect on carbon intensity. • Green finance has a ∩-shaped impact on carbon intensity in the eastern and western regions. [ABSTRACT FROM AUTHOR]

Transboundary lakes are shared by multiple administrative regions. The key to balance the development and protection of transboundary lakes is to properly measure the value of water resources. Most of previous studies on the measurement of lake water resources value have not fully considered the ecosystem service function. This paper proposes a new concept "composite water value" to measure the value of transboundary lakes by integrating the external runoff value and the internal runoff value of water resources. The study constructs a composite water value measurement system for transboundary lakes, further analyzes its influencing factors,and applies the system to the case of Nansi Lake, a representative transboundary lake in eastern China. The results show that: (1) The composite water value of lakes is influenced by various factors, including industrial structure, water withdrawal, and water use methods, which impact the external runoff water value; meanwhile, the composite water quality and fluctuations in lake level are closely associated with the internal runoff water value. From 2008 to 2021, the average annual composite water value of Nansi Lake was 39.628 billion yuan, exhibiting a "rising-falling-fluctuating rising" trend due to pollution control policies, reduced precipitation, and enhanced water-saving technologies successively. (2) From a long-term perspective, it is necessary to focus on the internal runoff water use value of lakes. The internal runoff water value of Nansi Lake has been over 75% of the composite water value, and flood storage and water conservation are important manifestations of its ecosystem service value. (3) The external runoff water value of lake is closely related to the internal runoff water value, and relevant departments need to consider the balance between the water withdrawal of multiple cities along the lake and the retained water volume of the lake to achieve the maximum benefit of composite water value. [Display omitted] • Lake water value includes multiple values derived from external runoff water out of lakes and internal runoff water in lakes. • The ecosystem service value of internal runoff water is much higher than the economic value of external runoff water. • There is a tradeoff relationship between the internal runoff water and external runoff water. • Composite water value measurement can help balance transboundary lakes development and protection. [ABSTRACT FROM AUTHOR]

Promoting the formation of the green lifestyle (GL) is a crucial step in achieving comprehensive green transformation of urban economic and social development. The widespread adoption of GL is influenced by various environmental regulations. Previous research mainly focused on the impact of individual policies on GL from the single policy perspective. The mechanisms of the combined effects of policies have not been thoroughly explored, particularly the contributions of each policy during periods of overlap. This paper takes the dual-policy of the New-type Urbanization Policy (NUP) and Smart City Policy (SCP) in China as an example. It employs panel data collected from 271 cities in China during 2007–2019 and establishes a multi-period difference-in-difference model to identify the combined effects of the dual-policy on residents' GL. Additionally, the Shapley value decomposition method is utilized to identify the contribution magnitude of each policy when they act simultaneously. The following conclusions are yielded. Firstly, the combined effects of dual-policy are more effective than a single policy in influencing residents' GL. Secondly, the Shapley value decomposition method reveals that when both policies are simultaneously implemented, SCP contributes a greater weight compared to NUP. Thirdly, the dual-policy can promote residents' adoption of GL through mechanisms such as green technological innovation, public participation in environmental protection, and the agglomeration of tertiary industries. Fourthly, the impact of dual-policy on residents' GL varies across different types and sizes of cities. This study attempts to unseal the "black box" of how the dual-policy influences residents' GL during the green transformation of cities in China, providing theoretical references for relevant urban policies in other countries and contributing to Chinese solutions and experience to global urban green development. • The dual-policy of NUP and SCP can jointly promote residents' green lifestyle. • The weight of SCP's contribution is higher than NUP's by the Shapely value method. • It explored three potential channels between the dual-policy and GL. • There are heterogeneous effects of dual-policy on GL in different types of cities. [ABSTRACT FROM AUTHOR]

Numerous studies have discussed the economic impacts of the COVID-19 pandemic in recent years. However, the effectiveness and trade-offs of diverse countermeasures still need to be investigated, particularly under the long-term goal of low-carbon transition, which is crucial for understanding the potential impacts of the future public health emergency (PHE) related economic crisis. Given that China still faces big pressures from the potential PHE and carbon neutrality, this paper assesses the effectiveness of policy instruments in restoring the economy and advancing green development after the PHE using the Dynamic Stochastic General Equilibrium framework. Our findings reveal that the PHE imposes more constraints on the economy because of the decrease in productivity on the supply side and in consumption on the demand side. Compared to the other counterparts, the mixed stimulus can overcome the adverse impacts of the PHE while contributing to carbon reduction. Furthermore, all types of low-carbon policies investigated in this study can contribute to carbon reduction at the expense of economic growth. Meanwhile, the carbon tax realizes the target of reducing emissions with the smallest negative impact on economic growth. Thus, we suggest adopting the carbon tax policy as the most effective low-carbon measure to address uncertainties associated with the PHE. • This study explores pathways for advancing economic recovery and green development after the public health emergency. • We develop a scenario-based dynamic stochastic general equilibrium (DSGE) model. • We characterize public health emergency shocks from a supply and demand perspective. • The model introduces different carbon abatement mechanisms. [ABSTRACT FROM AUTHOR]

An increasing number of countries and regions consider the bioeconomy transition a strategic policy priority. When approached through the lens of a circular economy perspective, investments in bioeconomy have the potential to enhance resource utilisation efficiency, preserve biodiversity and ecosystems, and foster sustainable development with low emissions. At the same time, if requirements and contextual factors of bioeconomy strategies are not formally analysed, bioeconomic investments might lead to unintended negative consequences. This paper proposes a decision support procedure to design, assess, prioritise, and monitor bioeconomy investments and policies. The flexibility and scalability of our decision support procedure is tested in Colombia to foster a regional and local transition to bioeconomy initiatives that consider the local capital assets and the stakeholders' views. The heterogeneous character of the Colombian environment, economy, society and culture represents an ideal condition to test the strength of the decision support procedure to promote bioeconomy in low and middle-income countries. Our empirical results highlight the benefit of adopting a formal assessment framework that includes strategic national indicators, regional features and stakeholders' views. In terms of the Colombian regional bioeconomy ambitions, we highlight the need for expanding knowledge hubs and participatory stakeholder networks and buttressing appropriate financial mechanisms. • Bioeconomy transition needs to be framed in an extended circular economy framework. • Bioeconomy strategies need to consider local context and stakeholders' engagement. • Scalable and replicable decision support procedures should guide bioeconomy implementations. • Balance sheet approach inform all stages of bioeconomy policies and projects. • Application of the balance sheet approach in Colombia highlights diverse local priorities. [ABSTRACT FROM AUTHOR]

Rapid developments in environmental infrastructure have contributed to significant improvements in green total factor productivity, but further investigation is required to provide a detailed assessment to understand the policy mechanisms involved. This paper analyzes environmental progress in China through MMQR, CCEMG, and AMG as empirical strategies for 30 provinces in China. Our empirical results reveal that energy optimization through renewable energy is the most effective channel to improve green total factor productivity, though it is not the only available option. Since environmental regulations, infrastructure development, and green technology innovation also directly impact energy efficiency, adopting these within policy channels will positively impact environmental sustainability. Our empirical approach helps suggest novel environmental policy suggestions. In particular, policymakers must introduce structural changes within energy developments to foster renewable energy. Furthermore, China must increase environmental spending to upgrade its energy infrastructure further and solve ecological issues. These insights offer valuable policy guidance for decision-makers in China and globally, aiming to foster economic and environmental sustainability and achieve zero-carbon transition goals. • The current study analyses the role of green factor productivity in zero carbon transition. • We use 30 chinese provincial datasets from 2005 to 2020. • Energy transition is the most effective channel to improve green total factor productivity. [ABSTRACT FROM AUTHOR]

This paper analyses the impacts of trading partners' environmental regulation on the export volumes of the Russian regions, using panel data covering about 140,000 observations in 84 Russian regions and 204 countries for the period 2013–2020. We employ the Gravity Model of international trade and Heckman Sample Selection methodology, with the fixed effects and random effects Poisson Pseudo-Maximum Likelihood techniques to analyse two subsamples of the Russian regions with different shares of environmentally sensitive goods in regional exports. As a robustness check, we utilize the non-parametric Driscoll–Kraay approach. The results of the econometric estimation demonstrate that environmental regulations enhance the export of regions with a high share of environmentally sensitive goods in export structure, while for the rest of the regions the environmental regulations negatively affect export volumes. The promoting effect is associated with a higher level of innovativeness of the regions and the role of the global energy transition process. We conclude that the environmental agenda is a challenge for some Russian regional exporters due to the lack of own environmental regulation, and to the specific features of the commodity and geographic structure of exports. To transform the negative impact into the positive one for all Russian regions, policy measures are necessary on the federal, regional, and company levels, encompassing environmental regulations and green development strategies, along with innovation and cluster policies, and the development of human capital and infrastructure. • Environmental regulations affect exports differently across Russian regions. • Innovative potential of regions is a key for positive effect. • Global energy transition can stimulate environmentally sensitive exports. • Solar energy positively affects exports, while wind energy is neutral. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has been proved to be an important engine of green economic development, yet how it will affect the internal structure of green economy is unknown. The aim of this study is to examine the impact and its mechanism of AI on green total factor productivity (GTFP) from the internal-structure perspective, by using provincial panel data of China from 2009 to 2021 and global Malmquist index. The main research results show that: (1) the development of AI contributes to China's GTFP growth. And this effect is more significant in undeveloped areas; (2) AI promotes China's GTFP growth mainly by improving resource allocation efficiency, while it exerts little impact through the paths of technological progress and scale efficiency; (3) the transmission mechanism of AI on GTFP varies greatly among China's three main regions. In the eastern region, AI improves GTFP mainly by both advancing technological progress and improving resource allocation efficiency, while in central region AI contributes to GTFP growth mainly through technological progress. Compared with the eastern and central regions, AI in the western region plays a stronger impact on GTFP through the channel of improving scale efficiency. This study helps to understand the pathways of artificial intelligence affecting the transformation of green economic growth and formulate differentiated regional policies in light of local conditions. • This paper studies the effects of AI on China's GTFP from internal-structural perspective. • GTFP is decomposed into three internal factors by the global Malmquist index. • AI has positive effect on GTFP through different impacts on internal factors. • Improving resource allocation efficiency is the most critical channel of AI on GTFP. • AI plays a more significant role in promoting GTFP in less developed areas. [ABSTRACT FROM AUTHOR]

In today's world, where economic development and environmental sustainability are becoming increasingly important aspects of national strategy, attention to the impact of different economic sectors on climate change is becoming an integral part of scientific research. This article focuses on analyzing the impact of primary and secondary economic sectors development on carbon dioxide (CO 2) emissions at the sub-national level in Russia from 2005 to 2019. The aim of the study is to provide an in-depth understanding of the relationships between the dynamics of these sectors and CO 2 emission levels in different regions of the country. Weighted regression and panel data methods were applied to better identify the patterns of the impact. The results show that the size of population and electricity consumption have the highest impact on CO 2 emissions. So that, the expansion of nuclear and gas generation capacity, as well as significant improvement of energy efficiency, are of crucial importance to reduce the emissions. Other sectors have a heterogeneous impact and requires more differential approaches, considering the specifics of regions. Taking into account the significant differences between the Russian constituent entities, this paper emphasizes the low informativeness of assessments at the national level and their inadequacy in terms of improving the efficiency of domestic management, including decarbonization policies. • The impact of considered economic sectors on CO 2 emissions were analyzed in 80 Russian constituent entities in 2005–2019. • Weighted regression and panel data methods were used to catch the specifics of Federal districts and constituent entities. • Population size and electricity consumption have the highest impact on CO 2 emissions. • Increasing energy efficiency and modernization of energy facilities are the most promising solutions to reduce the emissions. [ABSTRACT FROM AUTHOR]

To address global climate change, achieving carbon peak and carbon neutrality has become a global consensus. However, the means to simultaneously achieve carbon reduction and promote green economic development, particularly in developing countries, require further investigation. This study evaluates the impact of e-commerce on CO 2 emissions. Through an examination of the effects of the National E-Commerce Demonstration City (NEDC) policy from 2006 to 2017, this paper reveals that e-commerce growth facilitated by the NEDC policy resulted in a 7.89% reduction in total CO 2 emissions and a per capita reduction of 1.1146 tons in the pilot cities. Mechanism analysis demonstrates that the upgrading of industrial structure, development of digital finance, and the growth of innovation and entrepreneurship serve as primary pathways for this impact. The robustness of the findings is supported by parallel trend tests, placebo tests, and additional sensitivity analyses. Furthermore, the research reveals that the NEDC policy exhibits a more significant reduction in CO 2 emissions in cities with higher levels of economic development and non-resource-based cities. Welfare analyses show that the NEDC policy has significant socio-economic effects. These findings provide new evidence on the environmental effects of the digital economy and offer insights into achieving carbon neutrality. • The National E-commerce Demonstration Cities (NEDC) policy reduced CO 2 emissions. • Optimizing industrial structure, enhancing digital finance, and promoting innovation and entrepreneurship are mechanisms. • The effect is more pronounced in cities with high economic levels and high resource dependence. • The NEDC policy substantially enhanced social welfare by reducing CO 2 emissions. [ABSTRACT FROM AUTHOR]

Developing an efficient strategy for managing nutrients in less-developed lake watersheds that can balance the need for socioeconomic progress with the protection of aquatic ecosystems has become an urgent research subject for achieving sustainable development. This paper improves the optimization method for environmental and economic management of lake watersheds proposed in our previous research. A spatiotemporal optimization method based on a coupling model consisting of the Soil and Water Assessment Tool, system dynamics model, and objective programming model was applied to an agricultural non-point source (ANPS) pollution control program and a rural sewage treatment program at the Yilong Lake watershed as a case study. A simulation evaluation showed that the efficiency of the previous scheme was significantly improved after conducting spatiotemporal optimization. This scheme was dynamic and distributed, demonstrating an annual and high-resolution control program that can provide a basis for the precise management of ANPS. Although it still requires improvement, a framework for coupling simulation and two-step optimization was achieved in this study. • A framework for coupling simulation and two-step optimization is completed. • Spatiotemporal optimization could significantly improve the scheme, which is output by an objective programming model. • A dynamic and high-resolution NPS control program for paddy fields is obtained. [ABSTRACT FROM AUTHOR]

Practicing ESG concept is a necessary way for sustainable development of enterprises and an important hand in realizing high-quality economic growth. This paper introduces the hypothesis of "distance attenuation effect" of geo-economics into the research field of corporate ESG performance, and systematically examines the impact of neighboring the Environmental Protection Agency (EPA) on corporate ESG performance by using the data of China's listed companies in the manufacturing industry from 2011 to 2021. It is found that being a neighbor to the EPA has a significant inhibitory effect on corporate ESG performance, which was reduced by 0.196% when the distance between the two was reduced by every 1 km. And the inhibitory effect has a stepwise decreasing characteristic, i.e., it is the strongest at the level of county EPA, followed by municipal EPA, and the smallest at the level of provincial EPA. The mechanism analysis shows that the rent-seeking behavior caused by neighboring with EPA is an important reason for the decline of ESG performance, which verifies the "distance attenuation effect" of rent-seeking difficulty. Although neighboring the EPA improves firms' financial performance, it greatly harms firms' environmental, social and governance performance, which confirms the profit-seeking motive of firms' rent-seeking. In addition, the deterrent effect generated by third-party supervision and digital supervision can inhibit corporate rent-seeking to a certain extent, which helps weaken the negative impact of neighboring the EPA on corporate ESG performance. • Neighboring the EPA has a significant inhibitory effect on corporate ESG performance. • The rent-seeking behavior caused by neighboring with EPA is an important reason for the decline of ESG performance. • Neighboring the EPA improves firms' financial performance and greatly harms firms' environmental and social performance. • Public and media attention can helps to weaken the negative impact of being a neighbor of the EPA on firms' ESG performance. [ABSTRACT FROM AUTHOR]