Environmental Policy Formulation Research Articles

Coupling and interaction mechanism between green urbanization and tourism competitiveness based an empirical study in the Yellow River Basin of China

Exploring the spatial coupling relationship and interaction mechanism between green urbanization (GU) and tourism competitiveness (TC) is of great significance for promoting urban sustainable development. However, the lack of research on the interaction mechanism between GU and TC limits the formulation of effective environmental management policy and urban planning. Taking 734 counties in the Yellow River Basin (YRB) as the study area, this paper analyzes the spatial coupling relationship between GU and TC on the basis of comprehensive evaluation of GU and TC. Then, the interactive mechanism between GU and TC is systematically discussed, and the synergistic development strategy of the two is proposed. The results show that the GU level presents a multicore circle structure, with provincial capitals, prefecture-level urban districts and economically developed counties in east-central regions as high-value centers. The TC at county scale presents a multi-center spatial structure. Additionally, there is a significant positive spatial coupling between GU and TC in the YRB. The analysis further reveals that green urbanization level, social progress, population development, infrastructure construction, economic development quality, and eco-environmental protection has a observably influence on TC. Tourism competitiveness, service competitiveness, location competitiveness, resource competitiveness, market competitiveness, environmental influence, and talent competitiveness has a observably influence on GU. TC can promote GU, and the improvement of green urbanization level can support the development of tourism competitiveness. According to the spatial zoning method, 734 counties are divided into 6 categories, and the coordinated development strategy of GU and TC for each type of district is proposed.

A grey prediction model optimized by meta-heuristic algorithms and its application in forecasting carbon emissions from road fuel combustion

In Cameroon, predicting CO2 emissions from road transports is crucial since it contributes in formulating the government's energy plans and strategies. In this perspective, this paper proposes an optimized wavelet transform hausdorff multivariate grey model (OWTHGM(1,N)) that addresses some of the weaknesses of the classical GM(1,N) model such as inaccurate prediction and poor stability. In all, three improvements have been made to the classical GM(1,N): First, all inputs are filtered using the wavelet transform, thereby denoising variables that could hamper modelling; second, a new time response function is established using the Hausdorff derivative; and finally, the use of Rao's algorithm to optimise the model's parameters as well as the ξ-order accumulated value of the observation data described by the Hausdorff derivative. In order to demonstrate the effectiveness of OWTHGM(1,N), it is applied to predict CO2 emissions from road transport. The new model produces predictions with 1.27 % MAPE and 79.983 RMSE, and is therefore more accurate than competing models. OWTHGM(1,N) could therefore serve a reliable forecasting tool and used to monitor the evolution of CO2 emissions in Cameroon. The forecast results also serve as a sound foundation for the formulation of energy consumption strategies and environmental policies.

A cosine adaptive particle swarm optimization based long-short term memory method for urban green area prediction

In the quest for sustainable urban development, precise quantification of urban green space is paramount. This research delineates the implementation of a Cosine Adaptive Particle Swarm Optimization Long Short-Term Memory (CAPSO-LSTM) model, utilizing a comprehensive dataset from Beijing (1998–2021) to train and test the model. The CAPSO-LSTM model, which integrates a cosine adaptive mechanism into particle swarm optimization, advances the optimization of long short-term memory (LSTM) network hyperparameters. Comparative analyses are conducted against conventional LSTM and Partical Swarm Optimization (PSO)-LSTM frameworks, employing mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) as evaluative benchmarks. The findings indicate that the CAPSO-LSTM model exhibits a substantial improvement in prediction accuracy over the LSTM model, manifesting as a 66.33% decrease in MAE, a 73.78% decrease in RMSE, and a 57.14% decrease in MAPE. Similarly, when compared to the PSO-LSTM model, the CAPSO-LSTM model demonstrates a 58.36% decrease in MAE, a 65.39% decrease in RMSE, and a 50% decrease in MAPE. These results underscore the efficacy of the CAPSO-LSTM model in enhancing urban green space area prediction, suggesting its significant potential for aiding urban planning and environmental policy formulation.

Construction of landscape eco-geological risk assessment framework in coal mining area using multi-source remote sensing data

High-intensity and large-scale mining activities have aggravated regional eco-geological risk. Therefore, it is significantly essential to conduct an assessment of the eco-geological risk of mining areas. Although some progress has been achieved in ecological risk assessment studies, existing approaches are not entirely suitable for coal bases with high landscape fragmentation and dense coal mining activities. Here, we developed a novel landscape ecological and geological risk (LEGR) assessment framework based on theories that include landscape ecological risk and eco-geological risk. The framework selected 10 indicators, including slope, fluctuation, lithological hardness, soil type, FVC, RSEI, precipitation, biological abundance, distance to road and subsidence rate, and calculated the weights of indicators by introducing the AHP-CRITIC coupled weighting model. Then, the impact of landscape disturbances on eco-geological risk is quantified by measuring landscape losses. This framework was applied to the Shenfu mining area (SFMA), a typical coal base in northwest China. The results indicated the LEGR was moderate in the SFMA whose spatial distribution exhibited an increasing trend from southwest to northeast. Besides, the high LEGR was mainly in the aggregated mining area with high subsidence. For the eco-geological environment monitoring at the mine scale, a multiscale geographically weighted regression (MGWR) model was utilized for analyzing the relationship between indicators and LEGR within the disturbed range of coal mining. It provided valuable insights for the formulation of environmental protection policies in the mining area.

Emerging Technology Integration - Artificial Intelligence (AI) and Machine Learning (ML) for Predictive Analysis for Safety and Toxicity Assessment in Environmental Toxicology

Environmental toxicology is facing unprecedented challenges due to the escalating complexity of environmental issues. Traditional methodologies for safety and toxicity assessment are struggling to keep pace with the rapid evolution of pollutants and their impacts on ecosystems and human health. In response to this pressing need, there has been a paradigm shift in the field towards the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques. This research article delves into the transformative potential of AI and ML in environmental toxicology, elucidating how these advanced computational tools offer promising solutions for predictive analysis. By leveraging vast datasets and sophisticated algorithms, AI and ML enable more efficient, accurate, and timely assessment of environmental risks. They empower researchers and policymakers to anticipate and mitigate potential hazards before they escalate into crises. This paper provides a comprehensive overview of the diverse applications of AI and ML in environmental toxicology, ranging from predictive modeling of chemical hazards to early detection of environmental threats. Furthermore, it highlights the potential of AI and ML to revolutionize decision-making processes in environmental management and policy formulation. By synthesizing vast amounts of data and identifying complex patterns and correlations, these technologies offer invaluable insights for crafting evidence-based policies and strategies to safeguard the environment and public health. This research article underscores the pivotal role of AI and ML in addressing the multifaceted challenges of environmental toxicology. Through their integration, we have the opportunity to enhance our understanding of environmental risks, optimize resource allocation, and ultimately, pave the way towards a more sustainable and resilient future.

Preventing deviant behaviours in the formulation and implementation of environmental health policies in the era of self-media

National health could significantly impact political, economic and social spheres, having a critical role in national development, social stability and sustainable economic growth. The Chinese government is prioritizing health in all policies and promoting environmental health work. However, deviations in the implementation of China’s environmental health policy, such as excessive or incomplete execution, could hinder progress. Studies have identified the main factors that are inhibiting effective policy implementation; these include incomplete grassroots policy mechanisms, cognitive biases amongst policy executors, varying acceptance of policy execution objects and environmental complexity. To mitigate these issues, several actions have been recommended including improving government mechanisms, enhancing technical support, developing talents, increasing public scientific literacy and considering the needs of policy executors and environmental complexity. The Internet, especially self-media platforms, can enhance policy communication, reduce information bias and foster a favourable environment for policy implementation. By using self-media’s dissemination mechanism effectively, we can strengthen public consensus and improve public scientific literacy in environmental health. This contributes to eliminating policy formulation and implementation deviations, fully leveraging the guiding role of policies and promoting the construction of a Healthy China.

Peer effects of firm environmental protection expenditures

This study investigates the existence and influencing factors of peer effects in firms’ environmental protection expenditures (EPE). Using data from Chinese A-share listed companies from 2010 to 2021, we find that firm EPE decisions are positively influenced by the average level of peer firms within the same industry. Furthermore, we document that the peer effects of firm EPE are more significant when firms are covered by the new Environmental Protection Law and analysts. We also find that the peer effects of EPE can increase firm value, indicating that peer effects can enhance the scientificity and legitimacy of decision-making. To the best of our knowledge, our study is the first to investigate the influencing factors of firm EPE decisions from the perspective of peer effects, which can provide empirical evidence for firms’ green behavioral decisions and the formulation of environmental regulation policies.

The impact of geopolitical risk, institutional governance and green finance on attaining net-zero carbon emission

This research investigates the impact of geopolitical risk, institutional governance and green finance on environmental outcomes, specifically focusing on carbon emissions and ecological footprint. Utilizing the dynamic CS-ARDL method and aggregated mean group analysis on a panel dataset covering 21 nations from 2000 to 2021, our findings reveal that heightened geopolitical risk leads to both short and long run increases in carbon emissions and the ecological footprint. Our study finds both a direct as well as indirect connection between governance, green finance and environmental outcomes in both the short and long run, highlighting the nuanced impact of governance on the formulation of environmental policies and regulatory frameworks. The results emphasize the need for targeted strategies, including focused investments and incentives for sustainable finance, particularly in conflict-affected regions. Furthermore, our research underscores the enduring impact of historical events, such as wars, on contemporary environmental indicators, emphasizing the importance of proactive conflict prevention measures. Our research suggests that policymakers should adopt comprehensive strategies that prioritize emission reduction during short-run spikes in geopolitical risk while maintaining a steadfast commitment to long-run sustainability.

Game-theoretic modeling in regulating greenhouse gas emissions

This research introduces an innovative framework for addressing the escalating issue of greenhouse gas emissions through the integration of game theory with differential equations, proposing a novel model to simulate the regulatory dynamics between emission sources and legislative actions. By blending advanced mathematical modeling with environmental science, this paper underscores the critical necessity for pioneering, proactive strategies in environmental management and policy formulation. Central to our approach is the simulation of interactions within a game-theoretic context, aiming to delineate optimal strategies for emission sources and regulatory bodies, factoring in legislative constraints and environmental ramifications. The methodology employs a system of ordinary differential equations, capturing the dynamic, non-stationary nature of atmospheric processes and offering a realistic portrayal of the challenges in mitigating greenhouse gas emissions. Furthermore, the study introduces a fee-based regulatory mechanism designed to encourage emission reductions, highlighting the economic implications of such strategies. Significantly contributing to environmental management, this research presents a detailed model capable of predicting the trajectory of greenhouse gas emissions over a decade, considering the potential impact of technological innovations in emission control. The conclusion emphasizes the promising role of artificial intelligence in refining environmental governance, acknowledging the complexities and limitations inherent in predictive modeling. Aimed at policymakers and environmental scientists, this paper serves as a strategic tool for informed decision-making, advocating for a multidisciplinary approach to develop sustainable, effective solutions to combat one of the most critical environmental challenges facing the globe today.

Examining the dynamics between economic development, tourism, renewable energy and life expectancy in the Nordic economies

As the world struggles with pressing issues like climate change and sustainable development, affecting health outcomes and environmental quality, the Nordic regionsare at the forefront of major global challenges. This paper investigates the role of human capital, renewable energy use, tourism, natural resources, and economic growth in shaping life in the Nordic region i.e., Denmark, Norway, Sweden, Finland, and Iceland).Utilizing panel data spanning from 1990 to 2020, the Driscoll and Kraay standard error (DSK) technique is employed to analyze this intricate interplay. The study reveals that in the Nordic context, sustainable economic growth, bolstered by investments in human capital and the widespread acceptance of renewable energy sources, has been positively associated with increased life expectancies. Furthermore, prudent management of natural resources has helped mitigate adverse health effects related to depletion, maintaining environmental and public health standards. The thriving tourism industry has also been shown to influence lifespan in this region positively. On the contrary, the empirical finding contended that an adverse correlation exists between carbon emissions and LEX. This research underscores the importance of a comprehensive and balanced approach that considers economic development, sustainable development, and public health in pursuing longer and healthier lives, providing valuable insights for policymakers and regions seeking to replicate these positive outcomes.The findings of this study are both conceptually reliable and empirically robust, providing important insights for the formulation of environmental and health policy.

Ubuntu Philosophy for ecological education and environmental policy formulation

Abstract A world faced with global climate change needs actionable ways to curb its effects. We suggest that Ubuntu philosophy is a way to achieve peaceful coexistence between humans and the ecosystem. From an African perspective, we use Ubuntu philosophy’s concepts to understand how humans can create environmental policies and environmental education pedagogy. We use historical ecology and document review to explore the connections between humans, ecology and Ubuntu philosophy. An understanding of how philosophy and ecology relate is proposed to fill the gap in the cosmological premise between the African person and ecology. The study first sets out a relational model of humans and the ecosystem, then applies the ecology of education to environmental policy formulation. The research demonstrates that humanness with humaneness and collectivism, afro-eco-communitarianism, Ubuntu consensus politics, and Ubuntugogy are concepts underlying how the essence of Ubuntu philosophy can inform an eco-friendly society. The ethno-philo-cological underpinnings help conceptualize these concepts of community. Finally, the study’s conclusion shows how the concepts of Ubuntu philosophy suggest ways to improve environmental education and policy formulation. Climate concerns can be resolved by handing responsibility to the community.

Forest ecosystem on the edge: Mapping forest fragmentation susceptibility in Tuchola Forest, Poland

Forest ecosystems, vital for maintaining global biodiversity and ecological balance, are increasingly threatened by fragmentation. This study addresses the critical issue in the Tuchola Forest of Poland, examining the effects of natural and human factors on forest fragmentation. Our objective was to identify the most suitable dataset for monitoring forest fragmentation from 2015 to 2020, ascertain the primary drivers of fragmentation, and map the areas at high risk. Utilizing the PALSAR (25 m resolution) and Dynamic World (10 m resolution) datasets, we discovered PALSAR's enhanced ability to detect changes in forest structure, particularly evident after a significant windstorm in 2017. This dataset proved crucial in highlighting the escalating trend of forest fragmentation, reinforcing its importance for environmental monitoring and policy formulation. Our analysis identified key factors influencing fragmentation, such as proximity to croplands, tree height and age, wind speed, and vegetation water content, with areas near croplands and having younger, shorter trees being most susceptible. Employing a Weight-of-Evidence (WOE) Bayesian modeling technique, we mapped forest fragmentation susceptibility, demonstrating our methodology's effectiveness through high accuracy validation (AUC of 0.82 and Kappa Index of 0.68). Our innovative approach in mapping susceptibility to fragmentation, especially after extreme weather events, marks a pioneering contribution in Poland. This research advances the understanding of forest fragmentation dynamics and offers a scalable model for global application, emphasizing the urgent need for targeted conservation strategies to preserve the integrity of forest ecosystems amidst climatic risk and anthropogenic pressures.

THE POLITICS AND REALITY OF ENVIRONMENTAL JUSTICE IN INDIA: RESERVATIONS BETWEEN THEORY AND PRAXIS

This paper examines the dynamics of environmental justice in India, focusing on the disparities between theoretical ideals and practical implementation. The main objective is to critically analyse the intersection of environmental protection, social equity, and human rights in the Indian context and to identify the challenges and opportunities in achieving environmental justice. The paper highlights the persistent environmental injustices in India, despite the presence of robust legal frameworks. It explores the complex interplay between environmental governance, socio-economic factors, and institutional mechanisms, shedding light on the root causes of environmental disparities. The findings indicate that while India has made strides in environmental legislation and policy formulation, there are significant gaps in their execution and enforcement. Bureaucratic inertia, regulatory capture, and inadequate access to justice pose formidable barriers to achieving environmental justice. Moreover, the paper underscores the disproportionate impact of environmental degradation on vulnerable populations, exacerbating existing socio-economic inequalities.The study suggests that addressing these challenges requires a multi-faceted approach, including policy reforms, community empowerment initiatives, and enhanced institutional capacity building. It emphasizes the importance of democratizing environmental governance and amplifying the voices of marginalized communities in decision-making processes. The study contributes to a deeper understanding of the complexities and realities of environmental justice in India, offering insights into the gaps between theory and practice. It calls for concerted efforts to bridge these gaps and advance towards a more just and sustainable future for all.

Green investment in South Africa: A perception of overinvestment or underinvestment in energy and mining firms

This paper investigates green investments in energy and mining firms in South Africa to determine the efficiency level in terms of overinvestment and underinvestment. The general Richardson residual measurement model is employed, and an enhanced model is created by including variables that influence green investment, such as political connections and pollutant emissions. Data from 17 companies (5 energy and 12 mining) were used because of the significant effects of their operations on the environment over the period between 2015 and 2022. The study findings show that, in comparison to the estimated optimal investment level, South African energy and mining firms are not consistent regarding their investment level. It interplays between underinvestment and overinvestment. However, both firms demonstrated the tendency to green investment inefficiency due to underinvestment recorded in the latter years of the sample period. The study provides understanding as regards green investment levels of energy and mining firms and hence recommends adequate oversight and formulation of environmental policy by the government to ensure green investment efficiency in line with both national and international policies and regulations to facilitate a sustainable environment.

The gender and culture effect on the CO2 emission empirical analysis.

In recent years, the world has been facing severe challenges from climate change and environmental issues, with carbon dioxide emissions being considered one of the main driving factors. Many studies have proven that activities in various industries and fields have a significant impact on carbon dioxide emissions. However, few studies have explored the impact of gender on carbon dioxide emissions. This study aims to explore the potential impact of gender diversity on carbon dioxide emissions in the boards of directors of developed and emerging market enterprises. In addition, we also analyzed how board cultural diversity affects carbon dioxide emissions. We searched two European indices provided by Morgan Stanley Capital International (MSCI) from the Bloomberg database and conducted empirical analysis. We selected the MSCI index and MSCI emerging market index from 2010 to 2019 as samples and thoroughly cleaned up the data by removing any observations containing missing information on any variables. Statistical methods such as t-test, ordinary least squares, panel data analysis, regression analysis, and robustness testing were used for statistical analysis. At the same time, differential testing was conducted on sensitive and non-sensitive sectors, and the average representation of female boards in sensitive industries was low. The research results show that the proportion of female members on a company's board of directors is negatively correlated with carbon dioxide emissions. This discovery is consistent with the legitimacy theory advocating for gender equality and environmental sustainability, emphasizing the importance of gender diversity in reducing greenhouse gas emissions. However, agency theory suggests that diversity may lead to internal conflicts within a company, leading to agency costs and information asymmetry. The research results show a negative correlation between board cultural diversity and carbon dioxide emissions, indicating the potential challenge of board cultural diversity. This study provides important insights for decision-makers and managers, not only inspiring corporate social responsibility and environmental policy formulation, but also of great significance for academic research in the field of climate change. Our research findings help deepen our understanding of the factors that affect carbon dioxide emissions in different sectors and countries, while also expanding the research field between gender diversity, cultural diversity, and environmental sustainability. Although this study still needs to be further expanded and deepened, it provides useful insights into the relationship between board gender and cultural diversity and carbon dioxide emissions.

Estimation and Analysis of Air Pollutant Emissions from On-Road Vehicles in Changzhou, China

Vehicle emissions have become a significant contributor to urban air pollution. However, studies specific to city-level vehicle emission inventories are still scarce and tend to be outdated. This study introduces a methodology for developing high-resolution monthly vehicle emission inventories. We applied this methodology to Changzhou in 2022 to analyze emission characteristics and generate gridded emission data with a resolution of 0.01° × 0.01°. The results show that the total vehicle emissions of carbon monoxide (CO), volatile organic compounds (VOCs), nitrogen oxides (NOx), and fine particulate matters (PM2.5) in Changzhou are 39.69, 8.68, 18.6, and 0.56 Gg, respectively. Light-duty passenger vehicles are the main contributors to CO (74.3%) and VOCs (86.1%) emissions, while heavy-duty trucks play a significant role in NOx (50.7%) and PM2.5 (34.7%) emissions. Gasoline vehicles are mainly responsible for CO (78.6%) and VOCs (91.4%) emissions, while diesel vehicles are the primary source of NOx (81.1%) and PM2.5 (70.6%) emissions. Notably, China IV vehicles have the highest emission contribution rates (ranging from 32.5% to 44.9%). Seasonally, emissions peak in winter and are lowest in April. Spatially, emission intensity is higher in the northeast of Changzhou compared to the west and south. The methodology presented in this study offers a valuable tool for developing comprehensive city-level emission inventories, and the results provide critical insights that can inform the formulation of effective environmental policies.

The impact of digital economy on environmental pollution: Evidence from 267 cities in China.

Environmental pollution has become a pressing global issue, severely threatening human health and ecosystems. As an emerging driver of economic development in countries worldwide, the digital economy (DE) has the potential to enhance resource utilization efficiency and promote the development of clean technologies, thereby reducing environmental pollution. Based on the panel data of 267 cities in China from 2012 to 2021, the spatial econometric model is used to test the impact of DE on environmental pollution. The mediating effect model is used to explore the transmission mechanism of DE affecting environmental pollution. The panel threshold model is used to examine the threshold effect of marketization. The results are as follows: (1) DE can significantly reduce environmental pollution. The conclusion is still valid after conducting robustness tests such as selecting historical data as instrumental variables and the "Broadband China" pilot as a quasi-natural experiment. (2) From the perspective of transmission mechanism, DE can reduce environmental pollution through green technology innovation and industrial structure upgrading. (3) From the perspective of spatial spillover effect, DE can reduce the environmental pollution level of surrounding cities. (4) From the perspective of threshold effect, DE has obvious market-oriented single threshold effect on environmental pollution. When the marketization level crosses the threshold of 11.6611, the emission reduction effect of DE is significant. (5) From the perspective of heterogeneity, DE has a heterogeneous impact on environmental pollution in cities with different geographic locations, resource endowments and administrative level. Regarding geographical heterogeneity, DE can effectively reduce environmental pollution in eastern and central regions, but has no significant impact on environmental pollution in western regions. Regarding the heterogeneity of resource endowment, compared to non-resource-based cities, resource-based cities suffer more from the negative effects of DE on their environment. Regarding the heterogeneity of administrative levels, compared with non-central cities, the DE of central cities has a greater emission reduction effect. Based on empirical results, this paper proposes strategic recommendations in areas such as enhancing the application of DE in emission reduction, upgrading industrial structures, promoting green technology innovation, and improving the level of marketization. This study not only enriches the research of DE and environmental pollution, but also provides a reference for the formulation of environmental pollution control policies.

Vegetation net primary productivity in urban areas of China responded positively to the COVID-19 lockdown in spring 2020

To prevent the spread of COVID-19, China implemented large-scale lockdown measures in early 2020, resulting in a marked reduction in human activities over a short period. Studies have explored environmental changes during lockdowns, lacking analysis of response of net primary productivity (NPP) to lockdowns, especially for diverse vegetation types. Correlation between NPP and impact factors during lockdowns remains unclear. Through Google Earth Engine, we evaluated spatial-temporal changes in spring NPP at multiple scales during lockdown period (LD, 2020) compared with unlocked period (UL, 2017–2019) by remote sensing data in urban areas of China. Changes in four impact factors, aerosol optical depth (AOD) and photosynthetically active radiation (PAR) (via remote sensing data), alongside temperature (TEM) and precipitation (PRE) (via meteorological data) were explored. Additionally, geodetector, a valuable statistical tool for detecting the driving ability of various elements, was employed to explore the underlying causes of vegetation changes during LD. In the spring of LD: 1) National urban NPP generally increased (+6.50 %), notably in Northeast China (NE), North China (N) and East China (E). Besides, overall urban AOD decreased (−3.64 %), notably in N and Central China (C). National urban PAR increased (+2.7 %), particularly in C and Northwest China (NW). However, overall urban TEM (−0.06 %) and PRE (−1.21 %) changed negatively. 2) NPP in all three vegetation types in urban areas enhanced, with change rates: croplands > forests > grasslands. Evident enhancements occurred in the forests and croplands in N, and the grasslands in NE. 3) Through geodetector, during LD, AOD (q = 0.223) and TEM (q = 0.272) emerged as the dominant factors for NPP. Compared with UL, the explanatory power of AOD and PAR on NPP increased during LD. This study provides valuable insights into understanding the effects of short-term human activities on vegetation productivity, offering reference for the formulation of ecological and environmental policies.

The effects of environmental regulations on the sustainable entrepreneurship from the perspective of dynamic capabilities: a study based on Chinese new energy enterprises

Environmental regulations play important roles in enterprises’ sustainable entrepreneurship, and their relationships are affected by enterprises’ dynamic capabilities. This paper analyzed the panel data of China’s new energy industry from 2011 to 2021, aiming to explore the impact of environmental regulations on sustainable entrepreneurship and the mechanism of dynamic capacities between them. The results include: There is an inverted U-shaped relationship between incentive environmental regulation and enterprises’ sustainable entrepreneurial performances, and there is a positive relationship between command environmental regulations and enterprises’ sustainable entrepreneurial performances; Both absorptive capacity and innovative capability of dynamic capacities negatively moderate the inverted U-shaped relationship between incentive environmental regulations and sustainable entrepreneurial performances, and negatively moderate the positive relationship between command environmental regulations and sustainable entrepreneurial performances. The results highlight the importance of dynamic capabilities for new energy enterprises, and provide a certain enlightening effect on the formulation of environmental regulation policies, as well as the application of enterprises’ dynamic capabilities.

Environmental policy formulation through the establishment of food reserve regulations: Opportunities and challenges

Research into the social and environmental effects of Gorontalo Governor Regulation No. 48 of 2019, which addresses food storage, is detailed here. This research examines the extent to which these rules are in line with environmental responsibility and social fairness through a combination of normative juridical methodologies and policy analysis. The investigation revealed that, despite the best of intentions, the policies aimed at ensuring price stability and increasing food supply fail to adequately account for the impact they would have on the environment or involve local populations. For instance, the strategy does not take into account the effects of things like agricultural production patterns on environmental degradation and climate change or the carbon footprints of various distribution methods. The policy’s legitimacy and efficacy are further weakened by the limited space for public input in the design and implementation stages. The research suggests reworking regulations to incorporate criteria for ecological sustainability and public participation. This study aims to aid in the formulation of more efficient and sustainable food reserve policies by providing a more holistic understanding of food reserves as part of a larger ecological and social system.