Environmental Policy Formulation Research Articles

Effects of major air pollutants on cognitive function in middle-aged and elderly adults: Panel data evidence from China Health and Retirement Longitudinal Study.

Although numerous studies have discussed about the impact of air pollution on cognitive function, a consensus has yet to be reached, necessitating further exploration of their relationship. The aim of this study is to reveal the effects of major air pollutants on cognitive function in Chinese middle-aged and older adults, while considering the lagged effects of pollution. Panel data were constructed by integrating the air pollutants concentration (particulate matter diameter ≤1 µm (μm) (PM1), PM2.5, PM10, nitrogen dioxide (NO2), and ozone (O3)) among 28 provinces in China and the personal characteristics from China Health and Retirement Longitudinal Study participants during the period of 2011-2015. To explore the effects of single pollutants and their interactions on cognitive function, panel linear regression using ordinary least squares method was employed, and first-order lag effects (two-year interval) of air pollution were introduced into the models. Our study revealed that, after adjusting for confounding factors, higher levels of particulate matter (PM1, coefficient (Coef.) = -0.093, P = 0.001; PM2.5, Coef. = -0.051, P = 0.001; PM10, Coef. = -0.030, P = 0.001) and NO2 (Coef. = -0.094, P = 0.006) were associated with lower cognitive function scores among the participants. Moreover, the interaction between the five major pollutants exhibited a negative effect on cognitive function(Coef. = -2.89, P = 0.004). PM1, PM2.5, PM10 have detrimental effects on the cognitive function of middle-aged and elderly adults in China, where increasing particle diameter correlates with a less negative impacts, providing theoretical underpinnings for the formulation of environmental protection policies.

Predicting China’s Energy Consumption and CO2 Emissions by Employing a Novel Grey Model

The accurate prediction of China’s energy consumption and CO2 emissions is important for the formulation of energy and environmental policies and achieving carbon neutrality. This paper proposes a new weighted error evaluation criterion that emphasizes the importance of new data, thereby enabling more accurate capture of the variation characteristics of new data and reflecting the principle of new information priority. By optimizing the development coefficient, grey action, and parameters in the time response formula of the traditional GM(1,1), a novel optimized model, OGMW(1,1), is constructed. The accuracy of the new model is verified by three cases from the literature. The future trends of primary energy, oil, and coal consumption and CO2 emissions in China are predicted over the next five years. The conclusions are as follows: First, the new weighted error evaluation criteria are effective and reasonable and can indicate whether a grey model can reliably use the most recent information for modeling. Second, based on the new error evaluation criteria, the development coefficient, ash action, and parameter C in the time response function can be optimized. The results show that the optimization method is reasonable. Third, compared with the traditional models GM, GMO, and ARIMA, the OGMW(1,1) provides better simulation and prediction accuracy, and new information can be prioritized more effectively. Fourth, the forecasting results indicate that China will increase its consumption of primary energy, oil, and coal, as well as its CO2 emissions. Notably, the growth rates of primary energy and oil consumption are high at approximately 22.7% and 25%, coal consumption will increase slightly, and CO2 emissions will increase by approximately 11%.

Remote Sensing Inversion of Water Quality Grades Using a Stacked Generalization Approach

Understanding water quality is crucial for environmental management and policy formulation. However, existing methods for assessing water quality are often unable to fully integrate with multi-source remote sensing data. This study introduces a method that employs a stacking algorithm within the Google Earth Engine (GEE) for classifying water quality grades in the Songhua River Basin (SHRB). By leveraging the strengths of multiple machine learning models, the Stacked Generalization (SG) model achieved an accuracy of 91.67%, significantly enhancing classification performance compared to traditional approaches. Additionally, the analysis revealed substantial correlations between the normalized difference vegetation index (NDVI) and precipitation with water quality grades. These findings underscore the efficacy of this method for effective water quality monitoring and its implications for understanding the influence of natural factors on water pollution.

Estimates of Air Pollutant Emissions by Vehicle Fleet in the State of Pará, Brazil: Impacts and Trends

Objective: aims to estimate emissions of the main atmospheric pollutants from vehicular sources in the state of Pará, to continue the environmental agenda of monitoring air quality. Theoretical Framework: The growing global concern about air quality in urban areas is driven by the environmental and public health impacts caused by air pollution. The concentration of pollutants from vehicle sources represents one of the main challenges, especially in densely populated regions such as the capital and other major cities in the state of Pará, where the automotive fleet has increased significantly. Method: The methodology used combined "bottom-up" and "top-down" methods to calculate emissions of carbon monoxide (CO), nitrogen oxides (NO) and total hydrocarbons (HC) for different vehicle categories and fuel types. Data on licensed vehicle fleets and fuel consumption obtained from DETRAN-PA and ANP were used. Results and Discussion: The results revealed that gasoline-powered vehicles are responsible for the majority of CO and HC emissions, while diesel vehicles emit the largest amounts of NO into the atmosphere. Motorcycles were identified as the largest source of emissions due to their larger number in the circulating fleet. Our results also highlight the importance of assessing and monitoring vehicle emissions to develop effective air pollution control strategies. Research Implications: The information obtained in this study is unprecedented for the study area, and can therefore support the formulation of environmental policies aimed at reducing emissions of atmospheric pollutants in the state of Pará, thus promoting an improvement in air quality and public health. Originality/Value: This paper makes an unprecedented contribution to national and international literature on environmental pollution in large urban centers. On the eve of the largest climate and environment conference on the planet (COP30) to be held in Pará, Amazonas, this research joins efforts in relation to the monitoring, forecasting and mitigation of air pollution, considered one of the main environmental problems of today.

The Controversy of Social Forestry Policy: Public Reaction on the Ministry of Environment and Forestry Decree No. 287/2022/KHDPK in Java, Indonesia

Social forestry in Java, Indonesia, refers to policies and practices aimed at involving local communities in management and protection of forest areas. The study focuses on the controversy surrounding Decree No. 287/2022/KHDPK of the social forest policy which was rejected by local forest communities and public. The primary contribution of this research is to investigate the reasons behind the rejection on the Ministry of Environment and Forestry regarding Forest Areas with Special Management (KHDPK) policy, particularly the reduction in the forest area it entails. The main stakeholder involved in this controversy include Perum Perhutani, which has a vested interest in maintaining control of 2.4-million-hectare of forest land in Java. A qualitative research method was employed, using a case study approach applied for content analysis. Data were gathered from online newspapers and social media reports published between April 2022 and October 2022. The results indicates that the ongoing controversy reflects a disharmonious relationship between regulators and operators in implementing Agrarian Reform and Social Forestry policies on Java Island. The data suggest that the failure to manage the dialogue between stakeholders can create a challenging environment for the sustainability of both people and forests. Furthermore, involvement of stakeholders in the formulation and implementation of new environmental policies can foster better relationships. This study also highlights the critical role of dialogue and collaboration among regulators, operators, local communities, Non-Governmental Organizations (NGOs), and other stakeholders.

Unveiling the green path: How urban openness reduces pollution and paves the way to sustainability

This study investigates the relationship between openness and pollutant emission intensity across 286 Chinese cities from 1990 to 2019, aiming to evaluate the potential environmental benefits of open economy strategies. The findings indicate that enhanced urban openness significantly lowers pollutant emission intensity. To ensure the robustness of our findings, we make an innovative attempt to employ high-speed rail connection and motorway density as instrumental variables to address potential endogeneity issues, corroborating the reliability of the results through various robustness tests. Moreover, we also find the heterogeneous effects of urban openness on pollution emissions, highlighting the moderating influences of trade complexity, urbanization level, and environmental regulatory intensity. Lastly, the study elucidates the mechanisms through which urban openness diminishes pollution emissions, namely fostering green innovation capacity and enhancing public environmental awareness. This research makes theoretical contributions on understanding the nexus between open economies and environmental protection while offering practical insights to inform governmental environmental policy formulation.

Soil Organic Carbon Estimation via Remote Sensing and Machine Learning Techniques: Global Topic Modeling and Research Trend Exploration

Understanding and monitoring soil organic carbon (SOC) stocks is crucial for ecosystem carbon cycling, services, and addressing global environmental challenges. This study employs the BERTopic model and bibliometric trend analysis exploration to comprehensively analyze global SOC estimates. BERTopic, a topic modeling technique based on BERT (bidirectional encoder representatives from transformers), integrates recent advances in natural language processing. The research analyzed 1761 papers on SOC and remote sensing (RS), in addition to 490 related papers on machine learning (ML) techniques. BERTopic modeling identified nine research themes for SOC estimation using RS, emphasizing spectral prediction models, carbon cycle dynamics, and agricultural impacts on SOC. In contrast, for the literature on RS and ML it identified five thematic clusters: spatial forestry analysis, hyperspectral soil analysis, agricultural deep learning, the multitemporal imaging of farmland SOC, and RS platforms (Sentinel-2 and synthetic aperture radar, SAR). From 1991 to 2023, research on SOC estimation using RS and ML has evolved from basic mapping to topics like carbon sequestration and modeling with Sentinel-2A and big data. In summary, this study traces the historical growth and thematic evolution of SOC research, identifying synergies between RS and ML and focusing on SOC estimation with advanced ML techniques. These findings are critical to global ecosystem SOC assessments and environmental policy formulation.

Assessing the effects of different types of environmental supervision policies on green economy efficiency of China

Well-designed environmental regulatory tools are essential for environmental, economic, and social sustainability. This paper explores the effects of different types of environmental supervision policies on green economic efficiency in China from 2003 to 2021. In the first stage, the environmental supervision policies are divided into four types based on the governmental behavioral patterns and the composite index of environmental supervision policies is constructed based on multiple criteria, and the super-efficiency DEA-SBM model is used to measure green economic efficiency. In the second stage, the Tobit model is used to explore the effects of four different types of environmental supervision policies on green economic efficiency. The results show that: (1) Command policies inhibit green economic efficiency in the short term, while the long-term effect increases green economic efficiency. (2) Collaboration policies increase green economy efficiency from the second to the fifth year of implementation, but the subsequent long-term effects show a dampening effect. (3) Planning policies inhibit green economy efficiency in the short term, while they increase green economy efficiency after two years of implementation. (4) Guidance policies inhibit green economic efficiency in the short term but can enhance green economic efficiency after three years of implementation. The result provides theoretical guidance for the rational formulation and implementation of environmental supervision policies by environmental administrations in the future.

Projecting multiclass global land-use and land-cover change using deep learning and spherical geographic automata model

ABSTRACT Modelling land-use/landcover (LULC) change is vital for addressing global environmental and sustainability issues and evaluating various land system scenarios. However, existing geosimulation methodologies for global LULC change fail to account for spatial distortions caused by the Earth’s curvature and do not consider multiple LULC change processes. Thus, this research study proposes an enhanced spherical geosimulation modelling approach that integrates deep learning (DL) to simulate change of multiple classes of LULC process under the shared socioeconomic pathways (SSP) at the global level. Based on the simulation results, the frontiers of urbanization, cropland expansion, and deforestation are indicated to be in developing countries particularly in Asia and Africa. The simulation outputs also reveal 42.5%–63.2% of new urban development would occur on croplands. The proposed modelling approach can serve as a valuable tool for spatial decision-making and environmental policy formulation at the global level.

Environment court, shareholder conflict and corporate governance: evidence from market reactions to bank loan announcements

Purpose This paper aims to investigate the impact of environmental risk on corporate governance through market reaction to bank loan announcements. Design/methodology/approach Using the establishment of environment court in China as a quasi-natural experiment, this paper adopt the difference-in-differences approach based on listed firms during 2003–2013 to explore the impact of environment court on corporate governance. Findings This paper find that the environment court would weaken the cumulative abnormal return of loan announcements. Then, this paper confirm that the potential reason is that environment court worsens the interest conflict between majority and minority shareholders. Further, cross-sectional analysis suggests that bank’s supervision, market competition and analyst coverage can alleviate the impact of environment court on corporate governance. Practical implications Environment courts intensify firms’ internal interest disputes, thus causing the decrease of corporate governance, which can be observed through the effect of bank loan announcements. Social implications This paper provide reference for environmental policy formulation and implementation, firms’ decision-makings and improving the banking regulatory system. Originality/value This paper makes a contribution to the studies about the impact of environment court on firms’ decision-making and investors’ reaction, the impact of external factors on corporate governance and bank loan announcements effect.

Global Sensitivity Analysis of Soil Pollution Using Fractal Fractional Order Model

This research investigates the profound impact of land pollution on soil degradation, stemming from human-made (xenobiotic) chemicals and alterations in soil composition. The framework explains a comprehensive nonlinear fractal fractional order eco-epidemic model, delineating four compartments: Susceptible soil (S), Polluted soil (P), Remediation or recycling of polluted soil (T), and Recovered soil (R). The study rigorously establishes the non-negative and unique existence of solutions using the fixed point theorem while analyzing the local and global stability of equilibrium points under pollution-free equilibrium and pollution extinct equilibrium. Dula’s criterion confirms periodic orbits, while categorizing changes in secondary reproduction numbers provides crucial insights into pollution dynamics, enhancing our understanding of system dynamics. Local and global sensitivity analyses, employing forward sensitivity and the Morris Method, yield essential findings for informed decision-making. Additionally, Adams-Bashforth's method is employed to approximate solutions, facilitating the integration of theoretical concepts with practical applications. Supported by numerical simulations conducted in MATLAB, the study offers a nuanced understanding of parameter roles and validates theoretical propositions, ultimately contributing valuable insights to environmental management and policy formulation.

Impact of landscape pattern on habitat quality in the Yangtze River Economic Belt from 2000 to 2030

Recently, rapid economic development of the Yangtze River Economic Belt region in China and the growing problem of regional habitat quality have seriously threatened the process of biodiversity conservation in the Yangtze River Basin. Studying the impact of changes in landscape pattern indexes on habitat quality in the Yangtze River Economic Belt can provide policy insights for biodiversity conservation in the Yangtze River Basin. However, existing studies are still lacking in exploring the effects of changing landscape pattern indices on habitat quality under future scenarios. Based on the land use data of the Yangtze River Economic Belt from 2000 to 2020, this study simulates the land use situation in 2030, and analyzes the landscape pattern indexes and habitat quality during the 30 years, to investigate the impact of landscape pattern indexes on habitat quality at global and local scales through various methods. The results show that landscape patterns were significantly affected by human activities and land use types, with the Yangtze River Economic Belt experiencing increased levels of land use and structural stabilization during the three decades (Shannon Diversity Index increased from 0.90 in 2000 to 0.94 in 2030), fragmentation of the landscape (Landscape fragmentation index increase from 0.094 in 2000 to 0.102 in 2030), and weakened connectivity (contagion index decrease from 48.213 in 2000 to 45.437 in 2030). The Habitat Quality has a large variation in spatial distribution at the county scale, with an average change in habitat quality of 1.04 % from 2000 to 2030, and an overall downward trend. There is a strong localized spatial autocorrelation between landscape pattern indices and habitat quality, with significant spatial heterogeneity and some form of variability at time scales. The results of this study help to further understand the eco-environmental problems of the Yangtze River Economic Belt, and provide theoretical references for the formulation of ecological environmental protection policies and ecological functional area planning.

What factors influence individuals’ willingness to pay for environmental protection: evidence from CGSS2021

As global environmental issues become increasingly severe, research into individuals’ willingness to pay for environmental protection has gained prominence. This study aims to comprehensively explore the factors influencing individual willingness to pay for environmental protection in China. Utilizing the CGSS2021 data as a sample and employing hierarchical regression methods, we analyzed a large-scale, diverse dataset to examine factors affecting individual willingness to pay for environmental protection. The study reveals that income, environmental concern, knowledge, actions, public evaluation of government environmental efforts, and reliance on government environmental measures are significant factors influencing individual willingness to pay for environmental conservation. Additionally, the study conducted robustness checks on the model, verifying the stability and reliability of the results. This study not only provides a new perspective for academic research on environmental payment willingness but also offers more practical guidance for the formulation and implementation of environmental protection policies.

Application of Shannon Entropy in Assessing Changes in Precipitation Conditions and Temperature Based on Long-Term Sequences Using the Bootstrap Method

This study delves into the application of Shannon entropy to analyze the long-term variability in climate data, specifically focusing on precipitation and temperature. By employing data from 1901 to 2010 across 377 catchments worldwide, we investigated the dynamics of climate variables using the generalized extreme value (GEV) distribution and Shannon entropy measures. The methodology hinged on the robust bootstrap technique to accommodate the inherent uncertainties in climatic data, enhancing the reliability of our entropy estimates. Our analysis revealed significant trends in entropy values, suggesting variations in the unpredictability and complexity of climate behavior over the past century. These trends were critically assessed using non-parametric tests to discern the underlying patterns and potential shifts in climate extremes. The results underscore the profound implications of entropy trends in understanding climate variability and aiding the prediction of future climatic conditions. This research not only confirms the utility of Shannon entropy in climatological studies but also highlights its potential in enhancing our understanding of complex and chaotic climate systems. The study’s findings are vital for developing adaptive strategies in response to the evolving nature of climate extremes, thus contributing to more informed decision-making in environmental management and policy formulation.

A global comparative study on the impact of COVID-19 policy on atmospheric nitrogen dioxide (NO2): Evidence from remote sensing data in 2019–2022

A significant body of research has documented the profound changes in global atmospheric conditions during the COVID-19 pandemic. However, there is still an inadequate comprehensive comparison and assessment of countries before, during, and after the pandemic. Variations in restriction policies, human behaviors, and national traits lead to significant differences in how restriction policies affect atmospheric pollution. This study focuses on NO2, a pollutant with high temporal sensitivity, and utilizes the Oxford COVID-19 policy stringency index along with demographic information. Through spatial-temporal mapping, we analyzed NO2 emission fluctuations and calculated the emission changes in each country. Drawing from this analysis, we explored the relationships among these factors and found that over the span of 2019–2022, across 193 countries, global NO2 emissions displayed a distinct trajectory: initially decreasing, subsequently rebounding, and eventually fluctuating. Most countries exhibited seasonal variations in NO2 emissions. Additionally, the study uncovered a correlation between the stringency of COVID-19 policies and the reduction in NO2 emissions: as policies became stricter, emissions significantly decreased in most countries. In contrast, in countries with lower population densities, stricter policies paradoxically led to an increase in emissions. These findings underscore the importance of considering demographic factors and geographical context in the formulation and implementation of environmental policies.

The impact of ESG performance on corporate sustainable growth from the perspective of carbon sentiment

With the increasing importance of environmental and economic sustainability concerns, the concept of Environmental, Social, and Governance (ESG) has gained significant attention. In the era of digitalization, a research approach called carbon sentiment analysis has emerged as an innovative method. This study aims to explore the connections between carbon sentiment, ESG, and corporate sustainable growth within the context of the green economy. By using Ordinary Least Squares (OLS) regression analysis and establishing a panel data model of ESG performance and sustainable growth for Chinese listed companies, a notable positive correlation between the two variables was observed. Endogeneity was addressed using the two-stage instrumental variable method (2SLS) and the dynamic panel Generalized Method of Moments (GMM) model, with the results remaining robust both before and after the COVID-19 pandemic. Carbon-related news and textual information were collected and analyzed using advanced deep learning methods in Natural Language Processing (NLP), specifically Bidirectional Encoder Representations from Transformers (BERT) and Long Short-Term Memory (LSTM) models. This analysis enabled sentiment analysis and identification of the sentiment orientation of carbon news. The obtained sentiment scores were then integrated with company data to establish a moderation effect model. The findings of the study reveal that carbon sentiment significantly and positively moderates ESG performance in relation to corporate sustainable growth. Furthermore, the construction of a mediation effect model showed that carbon sentiment can moderate ESG performance by reducing environmental uncertainty, enhancing social trust, and alleviating financing constraints, thereby influencing corporate sustainable growth. The results of the heterogeneity group regression analysis demonstrate that the impact of ESG performance driven by carbon sentiment on sustainable growth is more pronounced in carbon market pilot regions, non-heavily polluting industries, and labor-intensive industries. This research provides a fresh perspective for understanding the dynamics of ESG, online carbon sentiment, and their implications for corporate sustainable growth. Additionally, it contributes to the development of the green economy and the formulation of environmental management policies.

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.