Environment, Development and Sustainability最新文献

Digital inclusive finance is a new pattern of financial development, and has been considered to be an effective means to promote the green economy. Therefore, exploring the relationship between digital inclusive finance and green economy is essential for sustainable development in China. This study evaluated Chinese provincial green economy using the China Provincial digital inclusive finance index, which is divided into three dimensions: breadth of coverage, depth of usage, and digitization level. Furthermore, we used multiple linear regression, spatial measurement, and mediation effect models to analyze the influence of provincial digital inclusive finance on green economy in China. The results revealed that digital inclusive finance can significantly improve the green economy. In addition, the impact of digital inclusive finance has spillover effects on the green economy, and the digital inclusive finance in a region can have significant impact on green economy in the surrounding areas. Breadth of coverage was found to be a direct mechanism for the effect of digital inclusive finance on green economy. The consumption structure of urban and rural residents plays an intermediary role between digital inclusive finance and green economy. Digital inclusive finance can promote the greening of residents’ consumption structure, and thus affect the greening of the economy.

Numerical simulation has emerged as a cost-effective strategy for addressing groundwater contamination challenges, a field that has witnessed remarkable advancements in recent decades. Despite this progress, a systematic review synthesizing the existing body of research is notably absent. To bridge this gap, we present the first holistic systematic review spanning 30 years of global groundwater pollution simulation studies, based on 1059 publications analyzed through bibliometric methods. Our study aims to dissect the knowledge structure, track research developments, and pinpoint frontier topics to guide future scholarly endeavors. Moving beyond traditional summaries, we quantitatively identified the spatiotemporal performance of research themes, including the most prolific publications, contributing institutions, renowned authors, and influential journals. Utilizing a comparative approach, we visually analyzed and mapped highly cooperative countries, domain disciplines, productive keywords, and research hotspots. Among the findings, China emerges as a leading contributor, considering the total publications, institutions, and core researchers involved. While conventional topics such as groundwater transport and nonpoint pollution continue to be prioritized, there is a growing emphasis on heavy metal, surrogate models, and climate change to meet evolving global environmental challenges. This research offers valuable insights for scholars, executives, and professionals, facilitating the development of economically and environmentally sustainable strategies to tackle groundwater pollution issues.

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This study explores the complex relationships among banking sustainability, financial development, and the ecological footprint in emerging economies, with a particular focus on public‒private partnerships (PPPs) in the energy sector. Using data from 16 emerging countries from 1990 to 2021 and the ARDL model, we highlight that banking sustainability initiatives supported by PPPs effectively reduce the ecological footprint in the short term. Our analysis introduces a novel definition of banking sustainability centered on energy investments through PPPs, offering an innovative perspective compared with traditional green finance approaches. Our results validate the environmental Kuznets curve (EKC) hypothesis, showing that, in the short term, PPPs promote the adoption of renewable energy technologies and circular economy practices, thereby improving environmental policies and supporting sustainable practices. However, we also observe a negative long-term effect, where initial benefits may be undermined by a shift in investments toward less sustainable sectors or external economic shocks. This dynamic underscores the importance of an integrated approach that combines economic growth, financial development, and environmental sustainability. We recommend that emerging economies strengthen banking sustainability policies with stringent standards for green investments, encourage PPPs in the energy sector, and align financial development strategies with environmental objectives. Our findings highlight the need for a holistic approach and innovative financial mechanisms to support sustainable economic growth while minimizing environmental impact.

Sustainability has become an inherent function of packaging due to the increased environmental impacts and growing waste. To mitigate these impacts, businesses must prioritize the implementation of Life cycle assessment (LCA) from the design phase to achieve the company's social, environmental, and economic goals through resource conservation and carbon reduction in order to meet carbon neutrality. However, companies face several barriers to the effective implementation of LCA. This study fills the gap by identifying, categorizing, and analyzing major barriers to LCA adoption for packaging decision-making in emerging economies- specifically in Indian contexts. To accomplish this research goal, an extensive literature review was undertaken for barrier identification. The Delphi technique was then used to find the relevant barriers, and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique was employed for barrier analysis to determine the interdependence among the barriers. The research findings revealed that there are seventeen barriers across four categories, where the “Limited understanding of LCA and its potential market advantages (OB3)” is the most significant barrier, followed by a “Lack of knowledgeable personnel of LCA in the packaging sector (OB2)” and “Lack of clarity and guidance of LCA methodology (MB1) in packaging area”, respectively as important barriers. This research broadens the scope of LCA in packaging design by overcoming significant barriers and helping to achieve packaging sustainability. This research provides an invaluable source of information for policymakers and packaging professionals to implement LCA effectively for packaging design and make more informed packaging decisions.

The rapid growth of new energy vehicles (NEV) has led to a significant increase in the retirement of power batteries (PBs), making PBs recycling an increasingly prominent issue. In order to encourage the recycling of retired PBs, the government has implemented a deposit policy, and NEV manufacturers have actively invested in advertising to fulfil the Extended Producer Responsibility. This study aims to investigate the effects of deposit policy and advertising investment on recycling decisions and model selection within the closed-loop supply chain (CLSC). Three recycling game models were constructed, both with and without the influence of the deposit policy. MATLAB software is used for numerical simulations, and the selection of the best recovery model is discussed from different perspectives. Results show that: (1) Under different recycling models, the wholesale prices of PBs and the sales prices of NEVs are equal, but the recycling prices and transfer prices differ. (2) The deposit policy and advertising investment both promote the forward decision-making of the CLSC but have the opposite impact on the reverse decision-making. (3) Considering economic benefits, environmental benefits, government surplus, and consumer surplus, it is evident that PB manufacturer and NEV manufacturer alliance recycling model yields relatively better results. (4) When incremental recycling falls below a certain threshold, NEV manufacturers need to carefully consider economic and environmental benefits when determining the levels of advertising investment. These findings can offer valuable suggestions for formulating government deposit policies, selecting power battery recycling models, and determining advertising investments for NEV manufacturers.

This paper aims at exploring whether and how environment, social responsibility, and corporate governance (ESG) affects firm operating efficiency (FOE). The potential moderating role of cash flow has also been studied in an effort to disentangle its relationships in this context. By using data on A-share listed companies from 2011 to 2020 in China, we find that a good ESG performance has a significantly positive impact on FOE. After changing the measurement of FOE, using Huazheng ESG performance and a series of stability tests such as endogenous tests, the results are still robust. Meanwhile, we find that the primary driver of the positive impact of ESG on FOE lies in its capacity to enhance economies of scale. In addition, we also find that ESG has a more pronounced effect on FOE for non-manufacturing enterprises and privately-owned enterprises. These results suggest that ESG has become an effective way to improve FOE. Exploring the impact of ESG performance on corporate operational efficiency is of significant practical significance for getting rid of corporate development difficulties, promoting good business operation, advancing the upgrading of the industrial structure of the industry, and ultimately realizing the efficient and steady growth of the industry as a whole.

The heavy reliance on input use in hazelnut production not only reduces economic efficiency but also threatens environmental sustainability, highlighting the need for improved input management and reduced ecological impact. This study aimed to assess the eco-efficiency of hazelnut farms using an input-oriented inverse DEA approach, determine the optimal input levels, and identify the factors contributing to eco-inefficiency through truncated regression analysis. Primary data were collected from 121 hazelnut farms through face-to-face interviews in the provinces of Giresun, Ordu, and Samsun in Türkiye. The inverse DEA method was used to evaluate eco-efficiency, while the truncated regression model was used to identify factors contributing to eco-inefficiency. The results revealed that the highest greenhouse gas (GHG) emissions in hazelnut production were caused by pesticides, contributing 153.58 kg CO_2-eq of GHG emissions per hectare. This was followed by manure at 132.53 kg CO_2-eq per hectare, chemical fertilizer at 78.44 kg CO_2-eq per hectare, and diesel fuel at 72.66 kg CO_2-eq per hectare. The average eco-efficiency score was calculated to be 82.8%, meaning that inputs could be reduced by 17.2% without affecting output. Pesticide use accounted for 24.83% of the total emissions, while manure contributed 21.42%, making them the largest sources of emissions. In hazelnut production, labor input was the most over-utilized resource, accounting for 12.40% of the total, followed by land size at 11.32% and other inputs at 10.53%. The eco-inefficiency model results showed that farming experience was statistically significant at the 10% level, agricultural insurance at the 5% level, and social security at the 1% level. The inefficient use of resources has a negative impact on both the economy and the environment. Therefore, implementing policies that promote the efficient use of inputs will significantly enhance the eco-efficiency of hazelnut production in Türkiye.

Improving carbon emission efficiency is crucial for tackling climate change and enhancing environmental quality. This paper uses a fixed-effects model to analyze whether energy inequality can enhance carbon emission efficiency across 270 Chinese cities from 2010 to 2019. This study finds that energy inequality exerts a significant positive impact on carbon emission efficiency, and this conclusion remains consistent even after a series of robustness tests. Environmental regulation serves to augment the effect of energy inequality on carbon emission efficiency, with market-based environmental regulation exerting the greatest influence and voluntary environmental regulation having the least impact. Heterogeneity analysis reveals that cities, characterized by resource-based economies, high-quality foreign direct investment and strong environmental preference, experience a more pronounced effect. Mechanism analysis supports the theoretical hypothesis that energy inequality can stimulate clean technology innovation, facilitate the realization of green industrial structure, and optimize energy consumption structure, consequently enhancing carbon emission efficiency. These findings contribute to the existing literature on energy inequality and carbon emission efficiency. Local governments should strengthen environmental regulations and actively promote green R&D investment to achieve continuous improvements in carbon emission efficiency. Additionally, future research can explore the impact of energy inequality on carbon emission efficiency from a more microscopic perspective.

The integration of Artificial Intelligence (AI) in the construction sector has opened new avenues for advancing Industrial Symbiosis (IS) research. However, existing literature lacks a comprehensive comparison of how leading AI digital assistants contribute to this field. This study addresses this gap by examining the performance of four prominent AI models, Gemini, CoPilot, ChatGPT-Classic, and ChatGPT-Advanced in generating responses related to IS opportunities in construction industry. The methodology involves a two-stage analysis: first, questions related to IS concepts and practices are posed to each AI model to test their response reproducibility, measured using BLEU, METEOR, and Cosine Similarity scores. This is followed by human expert evaluations to validate the quality of the responses. In the second stage, the models are tasked with defining the European Waste Catalogue (EWC) codes and Statistical Classification of Economic Activities in the European Community (NACE) sector classifications associated with the selected waste materials, followed by identifying potential IS opportunities. Key findings reveal significant variability in the models’ capabilities. ChatGPT models consistently demonstrate higher semantic alignment with expert evaluations in both the general questions and IS opportunity identification. In contrast, CoPilot shows strengths in syntactic accuracy but sometimes lacks depth in contextual understanding. The study also identifies that while some AI models are adept at defining waste codes and sector classifications, their ability to identify practical IS opportunities varies. These insights underscore the need for an integrated approach, combining AI-generated data with human expertise, to fully exploit IS potential in construction. This study not only sheds light on the current state of AI in IS identification but also provides a framework for evaluating AI models in similar contexts. Future studies should focus on enhancing AI models’ contextual understanding and broadening their applications to promote sustainable industrial practices across various sectors.