Comprehensive Indicator System Research Articles

Dynamic Multi-Factor Correlation Analysis for Prediction of Provincial Carbon Emissions in China’s Bohai Rim Region

This study presents a dynamic multi-factor correlation analysis method designed to predict provincial carbon dioxide emissions (CDE) within China’s Bohai Rim region, including Tianjin, Hebei, Shandong, and Liaoning. By employing the sliding window technique, dynamic correlation curves are computed between various influencing factors and CDE at different time intervals, thereby facilitating the identification of key feature attributes. A novel metric, the Consistency Index of Influencing Factors (CIIF), is introduced to evaluate the consistency of these factors across regions. Furthermore, the Accurate Predictive Capability Indicator (APCI) is defined to measure the impact of different feature categories on the prediction accuracy. The findings reveal that models relying on a single influencing factor exhibit limited accuracy, whereas combining multiple factors with diverse correlation features significantly improves the prediction accuracy. This study introduces a refined analytical framework and a comprehensive indicator system for CDE prediction. It enhances the understanding of the complex factors that influence CDE and provides a scientific rationale for implementing effective emission reduction strategies.

A two-stage framework for site selection of deep-sea offshore wind-to-hydrogen projects based on GIS - MULTIMOORA method under Pythagorean triangular fuzzy environment

Constructing deep-sea offshore wind-to-hydrogen projects (DS-OWHP) is crucial to solve the problem of offshore electricity storage and consumption. Addressing the uncertainty of the decision-making environment, and the lack of a comprehensive indicator system, this paper proposes a two-stage decision framework based on Geographic Information System (GIS). In the first stage, GIS is used to analyze the suitability of study area and identify alternative sites. In the second stage, the fuzzy MCDM method is used to further optimize alternative sites. Firstly, a comprehensive evaluation indicator system is established, and the Pythagorean Triangular Fuzzy Number (PTFN) is used to describe evaluation information. Then, a combination of stepwise weight assessment ratio analysis (SWARA), entropy, and game theory is used to determine weights, and the Multi-Objective Optimization on the basis of a Ratio Analysis plus the Full Multipli-cative form (MULTIMOORA) method is used to rank alternative sites. Finally, the feasibility of framework is proved through a case study in Guangdong Province. The results show that average wind speed has the highest weight of 0.149, and alternative site A2 which is close to zhuahai is the optimal choice. Through sensitivity analysis and comparative analysis, the reliability and stability of proposed model is verified.

Evaluating the Impact of Elementary School Urban Neighborhood Color on Children’s Mentalization of Emotions through Multi-Source Data

To address the challenge of quantitatively assessing the mentalization of emotions in color design schemes, this study uses Baidu Street View images and deep learning, integrates multi-source data, and innovatively constructs a color data model based on a comprehensive color indicator system for the quantitative assessment and visual representation of how the color environments of elementary school urban neighborhoods impact children’s mentalization of emotions. This model systematically incorporates physical color indicators, integrates elements such as perceptual frequency, and provides a novel perspective for color planning. The study’s results reveal that color metrics significantly impact children’s mentalization of emotions across multiple dimensions, with gender and age emerging as important influencing factors. Additionally, significant correlations were found between color and environmental elements such as building façades, roads, and signs. The study provides urban planners and architects with a practical color data model and recommendations for the revitalization of elementary school urban neighborhoods, offering a scientific basis for optimizing color design.

Ecological vulnerability assessment and spatial-temporal variations analysis in typical ecologically vulnerable areas of China

Ecological vulnerability assessment is crucial for environment protection, ecological restoration and resource utilization. However, many former studies have limitations in the indicator system of the assessment, which were not comparable for different types of ecologically vulnerable areas. It is difficult to apply directly to the ecological vulnerability assessment of different types and in various regions. Aiming to solve these problems, the study proposed a well-established and comprehensive indicator system for ecological vulnerability assessment and conducted ecological vulnerability assessment application of five types of typical ecologically vulnerable areas of China based on remote sensing, meteorological, geographic and other data. The results show that the average EVIs value of Zhangbei County ranging from 0.525 to 0.559 are the highest among the five research areas during the four periods, followed by Zoige region and Xiamen bay. However, the region with the lowest average EVI value varies. In 2005 and 2015, it was Taihe County, while in 2010 and 2020, it was the Sanjiangyuan region. The variation of average EVIs in the five typical areas presents slight fluctuation and remains generally stable from 2005 to 2020. It indicates that the environmental protection measures and projects undertaken by the Chinese government in recent years have had a striking effect, curbing the trend of ecological environment deterioration.

Asymmetric fiscal policies and digital economy development: An empirical analysis based on the global digital value chain perspective

The indicator system for evaluating the development level of the digital economy is multidimensional and dynamic. Accurate and scientific evaluation of the digital economy's development level is crucial. This study, based on the characteristics of the digital economy's development stages, evaluates the value creation dimension of the digital economy from the perspective of the global digital value chain. Additionally, it considers the access and usage dimensions to construct a comprehensive indicator system for evaluating the global digital economy development level. The results reveal significant disparities in digital economy development between countries, but also show that some developing countries have experienced rapid improvements in their digital economy development levels. In theoretical and empirical research, this paper utilizes the Barro Rule and the Cobb-Douglas production function, employing mathematical derivations combined with empirical regression analysis to explore the relationship and mechanisms between asymmetric fiscal policies and digital economy development during economic downturns. It finds that in the new era of the digital economy, governments often implement proactive asymmetric fiscal expenditure policies during downturns. Continuously promoting the development levels of national digital economies is an important mechanism for forming these characteristics of fiscal expenditures. Finally, this study proposes relevant policy recommendations, enriching academic research and policy discussions in the fields of financial fiscal policy and digital economy development.

A novel multi-source information fusion method for emergency spatial resilience assessment based on Dempster-Shafer theory

In the midst of today's intricate and ever-changing natural and social landscape, this study is committed to proposing a novel multi-source information fusion method for assessing the spatial resilience of urban emergency evacuation and rescue. Its overarching aim is to uncover latent challenges and contribute to the enhancement of a city's capacity to respond to emergencies. To achieve this, we have devised a comprehensive assessment indicator system, capable of not only quantifying a city's spatial resilience but also offering indispensable guidance for urban emergency evacuation and rescue spatial planning. Furthermore, we have introduced an innovative evaluation methodology framework. This framework includes the establishment of the basic probability assignment (BPA) model, a pioneering method for generating BPA for observed values, and the novel application of hierarchical weighting and fusion techniques. By extending the Dempster-Shafer theory, we have not only enhanced the method's ability to express and integrate uncertain information but also significantly improved the precision of the evaluation. Ultimately, we conducted a quantitative assessment using Shenzhen, China, as a case study, identifying existing issues and proposing highly-targeted improvement strategies. These research findings not only provide robust support for the augmentation of urban emergency capabilities in Shenzhen but also offer pioneering insights for the quantitative assessment and advancement of emergency spatial resilience in cities across the globe.

Identifying the risk factors of patient safety in internet hospitals: A mixed methods study

ObjectiveThe purpose of this study was to identify key risk factors and their interrelationships for patient safety in internet hospitals from a system perspective, using mixed methods of qualitative and quantitative analysis. MethodsThis study constructed a comprehensive indicator system of patient safety risk factors in internet hospitals by qualitative analysis using the Patient Safety Systems (SEIPS) model as a framework. Risk factors were initially identified through a literature review and subsequently refined using a Delphi survey involving 24 experts related to internet hospitals in China. The identified indicators were quantitatively analyzed to determine key risk factors and their influencing mechanism using the Decision Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) methods. ResultsThe qualitative analysis established a patient safety risk factor indicator system for internet hospitals, comprising 23 elements across six dimensions. Quantitative analysis employing the DEMATEL-ISM approach revealed that risk management has the highest centrality. Among cause factors, task complexity exerts the most significant impact on other factors, while network information security exhibits the highest absolute value among result factors. Risk factors are categorized into three levels: surface, deep, and root factors, with task complexity, legal and regulatory, and guidance policy being the root factors at the foundation of the system. ConclusionsOur study offered a systemic perspective on analyzing risk factors for patient safety in internet hospitals. Policymakers and managers of internet hospitals should take advantage of the interrelationships among these factors to mitigate patient safety risks by effectively controlling key factors. Public Interest SummaryIn the rapidly evolving landscape of internet hospitals, ensuring patient safety is paramount. This study aimed to comprehensively identify and understand key risk factors influencing patient safety within these digital healthcare platforms. Using mixed methods of qualitative and quantitative analysis, the study examined the intricate interplay of factors affecting patient safety. Our methodology involved constructing a risk factors indicator system based on the Patient Safety Systems (SEIPS) model. By employing the integrated Decision-Making Trial and Evaluation Laboratory along with the Interpretive Structural Modeling method, we unveiled the core risk factors and their intricate relationships. Recognizing the interconnectivity of these factors allows us to develop effective risk mitigation strategies that enhance patient safety in internet hospitals. This study encourages stakeholders to leverage the dynamic relationships among these factors to ensure safer online healthcare experiences for patients.

Energy poverty measurement and determinants in rural China: Does education inequality matter?

Due to the pressing need to attain sustainable development goal 7, global attention on addressing energy poverty has increased. Improving rural education has been recognized as a strategy to mitigate energy poverty in rural China. However, the presence of education inequality remains a substantial challenge to energy poverty alleviation and underexamined. Our research aims to evaluate rural energy poverty levels systematically and investigate the impact of education inequality on rural energy poverty. This study begins by employing a novel weighting approach to develop a comprehensive indicator system, which accurately measures the extent of energy poverty in rural China. Subsequently, a panel fixed-effects model is utilized to analyze the causal impact of education inequality on rural energy poverty. Furthermore, the study explores the heterogeneity and potential mechanisms of education inequality on rural energy poverty. Findings reveal a decline in rural energy poverty from 2005 to 2020, and the nature of energy poverty has substantially transformed, with a greater prominence of using unclean and unaffordable energy sources. Education inequality increases rural energy poverty and indirectly affects it by reducing the income of rural residents. The influence of education inequality on rural energy poverty exhibits heterogeneity in both time and region. Policy recommendations are proposed for mitigating energy poverty and promoting education equality in rural areas.

Research on the Impact of Digital Green Finance on Agricultural Green Total Factor Productivity: Evidence from China

Green development has become one of the important concepts leading China’s economic developments, and it is extremely meaningful to boost the continuous growth of agricultural green total factor productivity (AGTFP) to achieve the construction of a powerful agricultural country. Using China’s provincial data from 2011 to 2020, this manuscript calculates AGTFP through the SBM–GML model, and the digital green finance (DGF) through a comprehensive indicator system. The double fixed-effect model, quantile model and spatial Durbin model are used for in-depth study of the benchmark influence, the nonlinear effect and spatial spillover effect of DGF on AGTFP. The main research conclusions of the article are as follows: (1) DGF is significantly conducive to the improvement of AGTFP. Along with the continuous growth of AGTFP, the promoting effect of DGF has gradually increased. (2) In terms of impact path, green finance can properly promote the growth of AGTFP, while the role of the degree of digitization is not very significant. Meanwhile, the main channel for DGF to promote AGTFP is through green technology efficiency. (3) The impact of DGF on AGTFP varies spatially, while the role is more effective in regions with a higher degree of economic development and well-developed modernization. (4) There is a spatial spillover effect of DGF’s impact on AGTFP, which means that DGF can simultaneously promote the growth of AGTFP in local regions and neighboring regions.

Analysis of Spatio-Temporal Changes and Driving Factors of Wetland Ecosystem Health Based on the AHP-SOM-DPSR Model—A Case Study of Wetlands in the Qin-Mang River

Wetland ecosystems are vital for ecological security. However, rapid industrialization and urbanization have led to regional degradation, particularly in areas like the Qin-mang River Basin, a pivotal site in China’s ecological security strategy. Urgent measures are required to safeguard the health of its wetland ecosystems. This study employs the hierarchical analysis method-neural network-driver-pressure-state-response (AHP-SOM-DPSR) model to evaluate the ecological health of wetland ecosystems in the Qin-mang River Basin over a thirty-year period, from 1992 to 2022. Initially, the Drive-Pressure-State-Response (DPSR) model establishes a comprehensive indicator system encompassing 19 indicators spanning driving forces, pressures, states, and responses. Subsequently, a hybrid approach combining Analytic Hierarchy Process (AHP) and Self-Organizing Map (SOM) determines indicator weights to evaluate spatiotemporal ecosystem changes over the past three decades. Additionally, spatial autocorrelation theory analyzes ecosystem health in the study area. Finally, Pearson correlation coefficient analysis examines the driving factors influencing ecosystem health and their impacts. Results indicate: (1) Ecosystem health has deteriorated gradually from 1992 to 2022, underscoring the imperative for enhanced wetland management in the Qin-mang River Basin. (2) High spatial autocorrelation areas, primarily in the central-southern and northern regions, highlight priority zones for wetland ecosystem management. (3) Urbanization levels, average temperature, and total population significantly impact wetland ecosystem health in the Qin-mang River Basin. These findings offer valuable scientific insights for guiding ecological management and conservation efforts in the region.

Comprehensive River Health Evaluation Indicator System and Its Application

Comprehensive River Health Evaluation Indicator System and Its Application

Early warning of systemic risk in stock market based on EEMD-LSTM.

With the increasing importance of the stock market, it is of great practical significance to accurately describe the systemic risk of the stock market and conduct more accurate early warning research on it. However, the existing research on the systemic risk of the stock market lacks multi-dimensional factors, and there is still room for improvement in the forecasting model. Therefore, to further measure the systemic risk profile of the Chinese stock market, establish a risk early warning system suitable for the Chinese stock market, and improve the risk management awareness of investors and regulators. This paper proposes a combination model of EEMD-LSTM, which can describe the complex nonlinear interaction. Firstly, 35 stock market systemic risk indicators are selected from the perspectives of macroeconomic operation, market cross-contagion and the stock market itself to build a comprehensive indicator system that conforms to the reality of China. Furthermore, based on TEI@I complex system methodology, an EEMD-LSTM model is proposed. The EEMD method is adopted to decompose the composite index sequence into intrinsic mode function components (IMF) of different scales and one trend term. Then the LSTM algorithm is used to predicted and model the decomposed sub-sequences. Finally, the forecast result of the composite index is obtained through integration. The empirical results show that the stock market systemic risk index constructed in this paper can effectively identify important risk events within the sample period. In addition, compared with the benchmark model, the EEMD-LSTM model constructed in this paper shows a stronger early warning ability for systemic financial risks in the stock market.

Research on control of influencing factors of quality and safety of agricultural products on B2C e-commerce platform

In recent years, the quality and safety issues of agricultural products on B2C e-commerce platforms in China have become a new threat and concern, drawing widespread attention from the government and the public. This article aims to conduct an in-depth study on the factors influencing the quality and safety of agricultural products sold on B2C e-commerce platforms. First, various indicators are selected to construct a comprehensive indicator system for the factors affecting the quality and safety of agricultural products on B2C e-commerce platforms. Subsequently, factor analysis is applied to the 18 selected indicators using SPSS statistical software to derive a model for the influencing factors of quality and safety for agricultural products on B2C e-commerce platforms. Finally, based on the empirical analysis results, a comprehensive examination is conducted to assess the impact of each factor on the quality and safety of agricultural products on B2C e-commerce platforms. The empirical results indicate that the four major influencing factors, namely, production source, processing and manufacturing, warehousing and transportation, and regulatory measures, all play crucial roles in influencing the quality and safety of agricultural products on B2C e-commerce platforms.

Prevention or ex-post forcing? The impact of dual environmental regulation on corporate environmental behavior

Corporate reactions to environmental regulation are the hottest topics in research on corporate environmental behavior. However, a few studies incorporate other environmental behavior into the same framework. By constructing a comprehensive indicator system of dual environmental regulation, this paper uses a comparative analysis to discuss Chinese A-listed corporations' environmental behavior from 2009 to 2017, which were influenced by dual environmental regulation. The study's results show that formal environmental regulation (FER) has a significant U-shaped effect on pre-emptive environmental behavior (PEB) but an insignificant impact on ex-post environmental behavior (NEB). After categorizing the various forms of FER, this study finds that market-incentive environmental regulation has a significant inverted U-shaped effect on NEB but an insignificant impact on PEB, voluntary-participation regulation have a significant U-shaped effect on PEB and an inverted U-shaped effect on their NEB, and command-control regulation has significant influence on neither PEB nor NEB. In addition, informal environmental regulation (IER) has a significantly positive and negative effect on PEB and NEB. This study shows that corporate perceptions of policies can have a positive impact on the interaction between the FER and PEB but a negative impact on the interaction between the IER and PEB, and no impact on the interaction between FER or IER and NEB. Moreover, the impact of FER and IER on corporate environmental behavior (CEB) varies depending on factors like ownership, industry characteristics, the market environment, and regional development. Therefore, governments should understand the choices related to corporate environmental behavior under the dual environmental regulation—formal and informal—and prioritize the synergistic impact of these dual environmental regulation, highlighting their enforceability, and take into account the heterogeneity of their targets and the market to stimulate PEB and decrease NEB to help enterprises align their short-term economic objectives with their long-term social goals.

Study on agricultural drought disaster risk assessment in Heilongjiang reclamation area based on SSAPSO optimization projection pursuit model.

Heilongjiang reclamation area serves as a crucial hub for commodity grain production and strategic reserves in China, playing a vital role in maintaining national food security. Investigating the assessment of agricultural drought risk in this region can yield valuable insights into spatial and temporal variations in drought risk. Such insights can aid in formulating effective strategies for disaster prevention and mitigation, thereby minimizing food losses caused by drought disasters. This study employs a comprehensive indicator system comprising 17 indicators categorized into hazard, exposure, vulnerability, and resistance capacity. The projection pursuit model is applied to evaluate regional drought risk, while the PSO algorithm, optimized by the SSA algorithm, addresses the limitations of low local search ability and search accuracy during the large-scale search process of the PSO optimization algorithm. This study examines and compares the optimization and convergence capabilities of three algorithms: real number encoding-based genetic algorithm (RAGA), particle swarm optimization algorithm (PSO), and sparrow algorithm-based improved particle swarm optimization algorithm (SSAPSO). The analysis demonstrates that SSAPSO exhibits superior optimization performance and convergence properties, establishing it as a highly effective algorithm for optimization tasks. The findings reveal the following trends: over time, agricultural drought risk in Heilongjiang reclamation area has generally declined, with fluctuations observed in hazard and vulnerability, an increase in exposure, and a continuous enhancement of resistance capacity. Spatially, the western region exhibits significantly higher agricultural drought risk compared to the eastern region, primarily due to elevated hazard and vulnerability, coupled with lower resistance capacity. As the agricultural economy grows and agricultural expertise accumulates, the risk of agricultural drought decreases. However, variations in economic growth among different regions lead to diverse spatial distributions of risk.

Algorithm for community security risk assessment and influencing factors analysis by back propagation neural network

This paper aims to accurately assess and effectively manage various security risks in the community and overcome the challenges faced by traditional models in handling large amounts of features and high-dimensional data. Hence, this paper utilizes the back propagation neural network (BPNN) to optimize the security risk assessment model. A key challenge of researching community security risk assessment lies in accurately identifying and predicting a range of potential security threats. These threats may encompass natural disasters, public health crises, accidents, and social security issues. The intricate interplay of these risk factors, combined with the dynamic nature of community environments, presents difficulties for traditional risk assessment methodologies to address effectively. Initially, this paper delves into the factors influencing safety incidents within communities and establishes a comprehensive system of safety risk assessment indicators. Leveraging the adaptable and generalizable nature of the BPNN model, the paper proceeds to optimize the BPNN model, enhancing the security risk assessment model through this optimization. Subsequent comparison experiments with traditional models validate the rationality and effectiveness of the proposed model, with hidden layer nodes set at various levels like 10, 15, 20, 25, 30, and 35. These traditional models include Convolutional Neural Network (CNN), Long Short-Term Memory Network (LSTM), Bidirectional Encoder Representations from Transformers (BERT), Generative Pre-trained Transformer (GPT), and eXtreme Gradient Boosting (XGBOOST). Experimental findings demonstrate that with 20 hidden layer nodes, the optimized model achieves a remarkable final recognition accuracy of 99.1 %. Moreover, the optimized model exhibits significantly lower final function loss compared to models with different node numbers. Increasing the number of hidden layer nodes may diminish the optimized model's fit and accuracy. Comparison with traditional models reveals that the average accuracy of the optimized model in community risk identification reaches 98.5 %, with a maximum accuracy of 99.6 %. This marks an improvement of 9%–11 % in recognition accuracy across various risk factors compared to traditional models. Regarding system response time and resource utilization, the optimized model exhibits a response time ranging from 100 ms to 120 ms and consistently lower resource utilization rates across all scenarios, underscoring its efficiency in community security risk assessment. In conclusion, this experiment sheds light on the underlying mechanisms and patterns of community safety risk formation, offering novel perspectives and methodologies for researching community safety risk assessment. The paper concludes by presenting recommendations and strategies for addressing community safety risks based on experimental analysis.

Evaluation of the coordinated development level in the coastal eco-environmental complex system: A case study of Jiaozhou Bay, China

Given the exponential population growth and remarkable socio-economic advancements, coastal areas face increasingly complex challenges in eco-environmental management due to anthropogenic pressures. With the current emphasis on high-quality economic development, there is an urgent need to establish and evaluate a comprehensive indicator system to ensure the sustainable development of the coastal eco-environment and to meet evolving management demands. Research on the coordinated development level of coastal eco-environmental complex system, based on the concept of land-sea coordination, plays a pivotal role in promoting the resolution of eco-environmental issues in coastal areas, achieving sustainable socio-economic development in these regions. In this study, we construct an indicator system for the eco-environmental complex system in Jiaozhou Bay (JZB) coastal zone, China, comprising six sub-systems and thirty indicators. The comprehensive development level and coupling coordination degree model (CCDM) are employed in this study to analyze the indicator system in 1980–2020, aiming to elucidate the processes involved in the improvements in this complex system. The findings indicate: (i) the system's comprehensive development level evaluation and coupling coordination degree (CCD) exhibit a two-stage pattern: a declining trend in 1980–2005, followed by a rising trend in 2005–2020. (ii) despite improvements, the comprehensive development level and the CCD of the system in 2020 still hold potential for further enhancement compared to 1980; and (iii) policymaking and changes in anthropogenic pressures in coastal areas are the primary factors influencing the performance of the system. In the future, policymaking can reduce anthropogenic pressures on the coastal eco-environment, improve the comprehensive development level and CCD of the complex system, and encourage a commitment to sustainable development.

How do innovation factor allocation and institutional environment affect high-quality economic development? Evidence from China

An in-depth understanding of the relationship between innovation factor allocation, the institutional environment, and high-quality economic development can help us better comprehend how better productivity promotes high-quality, regionally coordinate, and balanced economic development. Using panel data of 30 provincial-level regions in China from 2011 to 2019, we explore the effect of innovation factor allocation on high-quality economic development and the moderating effects of different institutional environments. First, we construct a comprehensive indicator system for innovation factor allocation and a new model to calculate the weights of various indicators of innovation factors. Second, we apply TOPSIS and grey correlation to measure the level of innovation factor allocation, and construct a modified gravity model to generate a directed weighted spatial association network of this allocation as a spatial weight matrix. Finally, we use the spatial Durbin model to empirically test the spatial effects of innovation factor allocation on high-quality economic development under different institutional environments. We find that: First, innovation factor allocation significantly positively affects the high-quality development of the local economy, while it has negative spatial spillover effects on neighboring areas. The impact of different types of institutional environments on high-quality economic development exhibits an inverted “U” shape. China's institutional environment has not crossed the critical threshold of this “U" shaped relationship. Thus, different types of institutional environments can still effectively strengthen the role of innovation factor allocation in promoting high-quality economic development. In particular, the strengthening effect of online government is the strongest and its negative impact is the weakest. Overall, this study is of great significance for optimizing innovation factor allocation in various regions and promoting high-quality economic development.

Opportunities for China’s Agricultural Heritage Systems under the “Digital Nomadism” Trend—A Stakeholder-Weighted Approach

Agricultural heritage systems are appreciated worldwide for their significant environmental and cultural values. However, the sustainability of these systems has been compromised by environmental issues due to intensive human activities and socio-economic changes. Research has noticed that “digital nomadism” is providing a new opportunity for the sustainable development and transformation of these systems, regarded as “digital nomad-friendly” destinations. Nevertheless, comprehensive research on this new trend has been limited except for a few individual case studies. This study is the first to investigate the sustainable development of agricultural heritages considering the growing phenomenon of digital nomadism on a large scale. Based on in-depth stakeholder interviews, we introduced the Digital Nomad-Friendly (DNF) score—a comprehensive indicator system chosen and weighted by stakeholders. The DNF scores of 189 China National Important Agricultural Heritage System (China-NIAHS) sites were evaluated, and their distributions were analyzed on a national scale in China. The results highlight the importance of natural landscape and cultural value in the eyes of digital nomads, particularly emphasizing aesthetic landscape features, climatic suitability, and a community atmosphere. Sites with the highest DNF scores are located mostly in the Zhejiang and Yunnan provinces, sharing characteristics that appeal to digital nomads. The DNF scores exhibit regional variations, with high-DNF-score sites being concentrated in southwestern and southeastern China. By enhancing our understanding of digital nomads’ preferences and the influencing factors, this study provides valuable insights for the environmental management, policymaking, and sustainable development of China-NIAHS sites considering contemporary environmental and social changes.

The impacts of green finance on regional carbon emission：Evidence from China

<p>Green finance has become an important tool for the low-carbon development in China. This research constructs a comprehensive indicator system, and carefully describes the spatial characteristics of green finance and carbon emissions in China from 2012 to 2021. Then, the dynamic SDM and the mediating effect model are used to explore the spatial spillover effect, the mechanism and the heterogeneity. The research finds that carbon emission levels are generally higher in the north of China, and the level of green finance shows a spatial distribution of high in the east and low in the west. Moreover, green finance can significantly reduce carbon emissions in the presence of spatial spillovers with heterogeneity. For 1% increase in green finance within the region, per capita carbon emissions are reduced by 0.68% in the short term and 0.60% in the long term, and by 0.75% in the short term and 0.33% in the long term in the surrounding areas. In addition, green technology innovation has become an important way for green finance to boost low-carbon development.</p>