Tracking sustainable development efficiency with human-environmental system relationship: An application of DPSIR and super efficiency SBM model

Study on the Spatiotemporal Evolution and Driving Factors of Ecological Security in Stages Based on the DPSIRM-SBM Model: A Case Study of the Yangtze River Economic Belt

Scientific assessment of urban ecological security (ES) is an important prerequisite to realize regional sustainable development. Previous studies lack the consideration of quality and poor systematic correlation, which could not reflect the internal dynamic relationship. On the basis of considering the time lag, this study divided the research process into the natural operation stage and the management feedback stage based on the driving forces, pressures, state, impacts, responses, management (DPSIRM) framework model and DEA theory, so as to effectively overcome the above shortcomings. Finally, we analyzed the spatio-temporal characteristics and influencing factors of the ES level of 108 cities in the Yangtze River Economic Belt (YREB) during 2005-2019. The results showed that: (a) both two stages showed a slow and fluctuating upward trend in time series, and the level of urban ES in the management feedback stage was significantly higher than that in the natural operation stage; (b) with the passage of time, the spatial distribution of ES in the natural operation stage gradually developed towards the middle and downstream of the YREB, while the management feedback stage mainly evolved from the midstream to the edge area; (c) the level of urban ES presented a different degree of spatial agglomeration phenomenon, and showed an increasing trend over time; and (d) the key influencing factors gradually changed from pressure to response during 2005-2019. This research aims to provide an innovative perspective for the measurement of urban ES, and provide scientific reference for improving urban ecological sustainable development.

Constructing an evaluation model for the comprehensive level of sustainable development of provincial competitive sports in China based on DPSIR and MCDM

This study focuses on the objective assessment of sport development in socio-economic environments, considering the challenges faced by the industry. These challenges include disparities in regional investments, limited market participation, slow progress towards sports professionalization, and insufficient technological innovations. To tackle these challenges, we suggest implementing an integrated evaluation model that follows the DPSIR (Drivers, Pressures, States, Impacts, Responses) framework and incorporates comprehensive socioeconomic indicators. Subsequently, we utilized the Entropy power method and TOPSIS (Order Preference Technique for Similarity to an Ideal Solution, TOPSIS) analysis to comprehensively assess the progress of competitive sports development in 31 provinces and cities in China. Additionally, we recommended further developments in competitive sports and proposed precise strategies for promoting its growth. The framework and methodology developed in this paper provide an objective and scientifically based set of decision-making guidelines that can be adopted by government agencies and related industries in order to create successful plans that promote the sustainable growth of competitive sport. This is expected to bolster the nation's global influence, enhance social unity, and fuel economic expansion. The findings of this study offer policymakers valuable insights regarding competitive sports and can advance the development of the sports sector in China, thus making it a crucial driver of regional socio-economic progress.

Economic, environmental and social efficiency and effectiveness development in the sustainable crop agricultural sector: A systematic in-depth analysis review

Multi-dimensional inclusion of economic, environmental, and social sustainability spheres together are the most global concerns of the agricultural crop sector. Therefore, optimizing waste and natural resources guides researchers and policymakers to structure actions and strategies to attain sustainability. Several studies have been published around the world to choose between focusing on eco-efficiency or eco-effectiveness in different aspects. This work aims to systematically apply an updated review to critically assess the agricultural research articles' contributions among the assessment of those methods, models or tools, as well as a quantitative and qualitative in-depth analysis review to classify them, according to their mapping, functions, strengths, weaknesses, and logical relationships for the evaluation in the crop agricultural sector, which is expected to be needed in future to better understand the research gaps and select the appropriate methods for sustainability evaluation from different spheres (ecology, economy, and sociology). Of 242 peer-reviewed records from 2018 to the beginning of 2023, 135 reviews and articles gathered from Web of Science and Scopus meet the criteria to be examined. Our analysis revealed that the number of reviews is limited to approximately 4.5 %; most of the case studies were carried out in countries, such as China (36 %) and Brazil (6 %), and continents such as Europe (16 %). Depending on considered aspects, most studies evaluate the efficiency, effectiveness and derivatives using a set of tools, varying between the managerial tools applied for the macro-level structuration (DPSIR, EMA, and LCA) and mathematical tools applied for the micro-level quantification, subdivided into the visualization methods (GIS), and the optimization methods (DEA, SFA, MILP, FO). Thanks to their multifunctionality in considering different aspects of input, output and influence factors variables, the in-depth analysis study suggests the application of data envelopment and stochastic analysis to carry out a multidisciplinary evaluation for the socio-eco-efficiency or the socio-eco-effectiveness.

Analysis of the spatial-temporal evolution of Green and low carbon utilization efficiency of agricultural land in China and its influencing factors under the goal of carbon neutralization

Agricultural land is the most basic input factor for agricultural production and an essential component of terrestrial ecosystems, which plays a vital role in achieving carbon neutrality. Giving full play to the carbon-neutral contribution of agricultural land is a crucial part of China's economic transformation and green development. It incorporates carbon and pollution emissions from agricultural land use into the unexpected outputs of the Green and Low-carbon Utilization Efficiency of Agricultural Land (GLUEAL) evaluation system. The study utilized several advanced analytical tools, including the super-efficient Slacks-Based Measure (SBM) model, Exploratory Spatial-Temporal Data Analysis (ESTDA) method, Geodetector, and Geographically and Temporally Weighted Regression (GTWR) model. The objective was to examine the spatial-temporal evolution of GLUEAL and identify the factors that influenced it in all 31 provinces of China from 2005 to 2020. The results show that: (1) The overall spatial-temporal evolution of GLUEAL showed an increasing trend, but the disparity between provinces and regions became wider. (2) Most provinces have not yet made significant spatial and temporal jumps. They have high spatial cohesion with specific "path-dependent" characteristics. (3) The Geodetector results reveal that the Number of Rural Labor Force with Higher Education (NRLFHE) and Technology Support for Agriculture (TSA) have insufficient explanatory power on average for GLUEAL. Agricultural Economic Development Level (AEDL), Urbanization Level (UL), Multiple Crop Index (MCI), Planting Structure (PS), Degree of Crop Damage (DCD), Financial support for agriculture (FSA), and Agricultural mechanization level (AML) had stronger explanatory power on average for GLUEAL and were important factors influencing GLUEAL levels. (4) The average influence of AEDL, UL, FSA, and AML on GLUEAL changed from negative to positive. The average influence of MCI and DCD on GLUEAL was negative, and the average influence of PS on GLUEAL changed from positive to negative. This study provides a comprehensive description of the spatial and temporal evolution of GLUEAL in China. It reveals the key factors influencing GLUEAL and analyzes their spatial variations and impact patterns. These findings offer robust evidence for government policymakers to formulate policy measures for sustainable agricultural development and optimized resource allocation, promoting the transformation of agricultural land towards green and low-carbon practices and advancing the achievement of sustainable development goals.

Identifying the evolution of ecological poverty alleviation efficiency and its influencing factors: evidence from counties in Northeast China

Ecological poverty alleviation (EPA) is an effective strategy to address the vicious circle of poverty and environmental destruction in poor areas. However, it remains controversial whether this strategy has really succeeded in this respect. Previous research investigated the impact of a certain factor on EPA, and only few studies integrated them to explore their differential effects, thereby overlooking the complexity of EPA. Therefore, this study quantified the overall efficiency of the EPA strategies of 28 poor counties in three provinces of Northeast China from 2005 to 2018 by using a super-efficiency slacks-based measure model. This model can take into account undesirable outputs; as such, it has significant advantages in measuring the coordination among economic and social development and environmental protection. The Tobit model was used to explore the factors influencing EPA efficiency. The results show that, first, the majority of counties investigated had an EPA efficiency below the overall national average. Second, as for the factors influencing EPA efficiency, it was found that (1) GDP per capita and investment in environmental governance favored EPA efficiency, as they are conducive to stimulating regional consumption dynamics and achieving green economic development; (2) science and technology expenditure and urbanization were not conducive to EPA efficiency; and (3) industrial structure and trade had insignificant effects on EPA efficiency, due to the small scale of industry and the inadequacy of the policy system. This study assessed EPA efficiency from a holistic perspective, and addressed the controversies over EPA's influencing factors, thereby providing an effective method to conduct regional EPA assessment and improve EPA performance in poor regions of China.

An empirical assessment of the impact of digital economy and environmental regulation on regional water resources efficiency in the context of COP26

The 26th Conference of the Parties to the United Nations Framework Convention on Climate Change (COP26) highlighted the importance of nuclear techniques in mitigating the impact of climate change on water resources, and improvements in water efficiency were considered an important step towards the achievement of the COP26 goals. We selected the super-efficiency SBM method to measure water resource utilization efficiency between the period of 2007 and 2020, and we focused on the relationship between the digital economy and water resource use efficiency (WRE). Conclusions as follows: (1) China's water resource use efficiency value is 0.441. The water resource use efficiency in the eastern region is the highest, and that in the western region is the lowest. (2) The digital economy can significantly promote the improvement of water resource use efficiency in the whole country and the eastern region. The impact of the digital economy on water resource use efficiency in the central region is not significant, and that in the western region is inhibitory. (3) In addition to the central region, environmental regulations in the east, west, and the whole country have made positive contributions to the efficiency of green water resource use. (4) Economic development promotes WRE in the whole country, the east, and the middle, but not in the west. The impact of industry on WRE is always negative. The impact of technological progress on WRE in the central and western regions and the whole country is not significant, but the impact in the eastern region is positive. The level of opening to the outside world has no significant impact on the WRE of the eastern region, but negative impact on the WRE of other regions. Abundance of water resources has a negative impact on WRE in all regions.

Sustainability performance evaluation involving internal resource imbalance: an additive aggregation network DEA approach

Environmental pollution, as a byproduct of economic growth, causes negative pressure on human health. Its sustainability management performance is closely bound up with the ecological carrying capacity. Due to the limited carrying capacity of ecosystems to pollutants, the hidden costs of pollutants may increase when pollutants flow and spread. This is an external manifestation of the internal resource imbalance within the ecosystem, restricting the sustainability of economy and environment and overlooked by most studies that target sustainability performance evaluation. Thus, this study considers the internal resource imbalance during the sustainability performance evaluation for the first time in the context of the interaction among economy, environment, and human health, by constructing a production-treatment-health framework, proposing an internal resource imbalance index and developing an additive aggregation network data envelopment analysis model with the semidefinite programming technology. This study takes 30 Chinese provinces from 2012 to 2017 as the research objects and confirms the effectiveness of sustainability management in terms of water pollution purification and water ecological construction.

A systematic coupling analysis framework and multi-stage interaction mechanism between urban land use efficiency and ecological carrying capacity

Since urban land use efficiency (ULUE) bridges urbanization and economic efficiency while ecological carrying capacity (ECC) is the basic natural endowments support, the coupling coordination degree (CCD) between ULUE and ECC represents a combination of resource-intensive and environment-friendly, which can serve as an effective tool to evaluate sustainable development. We first quantified ULUE and ECC by super-efficiency DEA, DPSIR framework, and entropy-TOPSIS from a coupling perspective, attempting to compensate for the lack of clarity regarding urban sustainability constraint factors in the holistic perspective. On this basis, we formulate an integrated coupling coordination analysis framework comprising temporal and spatial characteristics, disorder diagnosis and interaction mechanism to synthesize the current scattered research directions into a logically clear framework and serve as a guide for future research on coupling. Moreover, to extend the macroscopic mechanism to a microscopic level at a theoretical level and facilitate more effective and sustainable urban management practices, this paper highlights a detailed multi-stage coupling mechanism corresponding to different stages of urban development, deriving an urban sustainable development spiral upward model. The results indicated that the CCD between ULUE and ECC exhibits a significant clustering pattern accompanied by a spatial spillover effect, which was closely related to economic development level and natural resource endowment. Besides, the disorder factor in the eastern Jilin province was ULUE while the western was ECC. Furthermore, the ULUE will take precedence over ECC breaking the old balance, in which technological innovation is the internal driving factor. These findings also illustrate the analysis framework and coupling mechanism mentioned in this paper can act as a nexus between interdisciplinary perspectives to enhance our understanding of changing social-ecological systems, thus serving urban sustainable development.

A spatial effect study on digital economy affecting the green total factor productivity in the Yangtze River Economic Belt

With the advent of the Industry 4.0 era, the digital economy (DE) has become a new driver of sustainable development. This paper focuses on DE's green and environmental value. Based on the panel data of 108 cities in the Yangtze River Economic Belt from 2011 to 2019, we evaluated the temporal and spatial evolution of DE and green total factor productivity (GTFP). The spatial Durbin model analyzes the direct and spatial spillover effects of DE on GTFP, the green efficiency change (GEC), and the green technical change (GTC). The results show that the DE maintains a stable growth trend. GTFP and GTC show a similar fluctuating upward trend, while GEC shows a fluctuating downward trend. Both DE and GTFP show a clustering trend of "high in the lower reaches and low in the upper and middle reaches" and a typical center-periphery pattern over time. Second, for the direct effect, DE has a significant positive impact on GTFP, mainly on GTC rather than GEC. The promotion effect is stronger at the new normal stage, in the lower reaches, and the three major urban agglomerations. Third, for the spatial spillover effect, DE has a trickle-down effect on GTFP and GTC and a siphon effect on GEC, more potent at the new normal stage and in the lower reaches. Compared with peripheral cities, DE has significant trickle-down effects on GTFP, GTC, and GEC in the three major urban agglomerations.

Identifying the impact factors of sustainable development efficiency: integrating environmental degradation, population density, industrial structure, GDP per capita, urbanization, and technology

To accomplish the high-quality development target in Yellow River Basin, the current study investigates the impact factors of the rural sustainable development efficiency in Yellow River Basin from the period of 1997 to 2017, by using Super efficiency Slack-based Measure, improved STIRPAT, and the OLS regression. The findings illustrate that rural sustainable development efficiency in Yellow River Basin is maintaining a fluctuating upward trend during the investigation. The impact factor analysis reveals that at the entire basin level, the population density and industrial structure have the greatest impact on rural sustainable development efficiency, while the technology level has the least impact. The industrial structure and GDP per capita negatively impacted rural sustainable development efficiency in the upper and middle basin, while they have non-significant positive impact in the lower basin. Besides, urbanization level inhibited rural sustainable development efficiency in upper basin (except middle basin and lower basin), and technology level has promotional effect in rural sustainable development efficiency at the entire basin as well as at the 3 sub-basins, while the influence effect is not significant in the lower basin. Therefore, these empirical results indicate that the impact effect of these factors exist spatial heterogeneity. Thus, decision-makers should consider this reality fully and make differential measures when they construct the development long-term strategies for rural sustainable development efficiency in yellow river basin.

Comprehensive Analysis of Grain Production Based on Three-Stage Super-SBM DEA and Machine Learning in Hexi Corridor, China

Food security is always a pressing agenda worldwide. The grain production in many areas has decreased due to the reduction in agricultural research funding and infrastructure investment. In this paper, we employed the Extreme-Tree algorithm to determine the main effectors in grain production in Hexi Corridor, Gansu, China, during 2002–2018. First, we applied the three-stage super-SBM DEA to precisely assess agricultural production. Then, we used the Extremely randomized trees algorithm to quantify the importance of each factor. Our results show that the variant of average efficiency score at the first stage was minimal. After removing the influence of environmental factors on production efficiency, the more accurate efficiency score was decreasing from 2002 to 2018. The R2 value of the Extra-Tree model was 0.989 in the grain production analysis. Our research shows that grain production in the Hexi Corridor was controlled by human-driven but not nature-driven during our research period. Based on the importance attribution analysis of each model, it showed that the importance of human-driven investment occupied 93.7% of grain production. The importance of nature-driving was about 6.3%. Accordingly, we proposed corresponding opinions and suggestions to government and growers.

The Evolution of the Spatial-Temporal Differences of Municipal Solid Waste Carbon Emission Efficiency in China

Municipal solid waste (MSW) treatment is one of the major contributors to carbon emissions. The improvement in MSW treatment carbon emission efficiency is crucial for China to achieve its CO2 emission targets. Firstly, this study used the super-efficiency SBM-DEA model to calculate the MSW treatment carbon emission efficiency in 31 provinces in China from 2010 to 2019. The results show that the MSW treatment carbon emission efficiency in all provinces except Shanghai and Jiangsu is less than 1, and the provinces with high efficiency are mainly located in eastern China. Secondly, the spatial auto correlation model and spatial Markov chain are used to test the regional differences and the spatial spillover effect of efficiency. The results show that the national average efficiency shows a fluctuating downward trend, and only the western region achieves a gradual increase. The regional differences in China’s MSW processing efficiency of carbon emissions show a fluctuating upward trend, and the regional background affects the spatiotemporal evolution pattern of the efficiency. Finally, the special error model was used to analyze the factors and influence paths that affect the efficiency, and to find that the degree of government intervention as an influencing factor that restricts the improvement of efficiency. Based on the research results, we put forward countermeasures and suggestions to improve the MSW treatment carbon emission efficiency in each province and the country as a whole.

Prioritizing stakeholders' preferences for policy scenarios of vulnerable ecosystems with spatial heterogeneity in choice experiment: Coupling stated preferences with elevation

Understanding public preferences and evaluating the river basin are essential for effective river basin management, and enhancing its environmental attributes can provide considerable non-market benefits. As such, the study explores the heterogeneity in people's preferences and rankings of river ecosystem services based on their willingness to pay (WTP) to upgrade these services. A research survey was conducted throughout the river basin using a choice experiment approach. In this study, we evaluated the impact of study area elevation (a spatial attribute) on residents' willingness to pay for rehabilitation of environmental attributes. The study incorporates 6 ecological attributes in order to examine the differences in people's willingness to pay at various elevation levels. A total of five cities and 33 surrounding villages and townships were surveyed, while five elevation groups were made on an ad hoc basis to split samples, i.e., 1000-1600 m, ≤1600-2200 m, ≤2200-2800 m, > 2800-3400 m, and 3400-4000 m. The results of the mixed logit model recognized that people living at different elevations value rehabilitation of varying environmental attributes differently. For example, the inhabitants in Group 1 (1000-1600 mm) are willing to pay RMB 6.70 per year for biodiversity upgrades; while the WTP of the people for the same attributes is RMB 32.68 in Group 5 (3400-4000 mm). The Krinsky Robb approach confirmed that agricultural product quality and greenhouse gases (GHGs) were the most highly valued attributes, with a willingness to pay of RMB 90.40 and RMB 47.17, respectively. Applying these results as a reference for sustainable improvements and uplift of deteriorated ecological qualities is an example of how they may be helpful in bettering the world.

Carbon emission efficiency measurement and influencing factor analysis of nine provinces in the Yellow River basin: based on SBM-DDF model and Tobit-CCD model

The Yellow River basin (YRB) is China's most critical energy consumption and coal production area. The improvement of carbon emission reduction efficiency in this area is the key for the Chinese government to achieve the 2030 carbon peak and 2060 carbon neutral ("30.60"). Given this, this study first calculates the carbon emission efficiency of YRB from 2005 to 2019 based on the slack-based measured directional distance function (SBM-DDF) model and combined with Malmquist-Luenberger (ML) index and decomposes the carbon emission efficiency of each province. Then, a panel Tobit model with random effect is constructed to measure the influencing factors and their influence degree of carbon emission efficiency of YRB. Finally, the main influencing factors are selected, and policy suggestions on how to improve the carbon emission efficiency of each province are put forward with the help of the coupling coordination degree (CCD) model. The results show that first, the carbon emission efficiency of each province is significantly different, but it shows a fluctuating upward trend on the whole. Second, the reasons for the rise or decline of the ML index in different provinces are different. Therefore, the development strategies of different provinces should be formulated from the perspective of accelerating technological progress and improving technical efficiency. Finally, the calculation results of influencing factors and coupling coordination degrees show that provinces with high coupling coordination degrees should focus on developing per capita power consumption and controlling per capita power consumption to consolidate the actual urbanization process and industrial structure adjustment. Provinces with low coupling coordination degrees should focus on maintaining the urbanization process and increasing the development of the tertiary industry. Therefore, to fundamentally reduce carbon emissions in YRB areas, we need to consider implementing differentiated emission reduction schemes based on national strategic objectives and in combination with the development characteristics of various provinces.

Technological Innovation, Fiscal Decentralization, Green Development Efficiency: Based on Spatial Effect and Moderating Effect

Green development efficiency is an essential measure of China’s economy turning into a stage of high-quality development in the new era. This paper establishes a spatial Durbin model based on the new geography economics. It empirically investigates the spatial effect of technological innovation on regional green development efficiency and the moderating effect of fiscal decentralization on the above mechanism using panel data of 29 provinces in China from 2010 to 2018. The results show that: from 2010 to 2018, both technological innovation and green development efficiency in Chinese provinces show significant spatial clustering effects; technological innovation not only has a significant role in promoting green development efficiency in the region but also leads to the improvement of green development efficiency in neighboring regions; and fiscal decentralization positively regulates the direct effect of technological innovation on green development efficiency in the region, and negatively regulates the spatial spillover effect of technological innovation on green development efficiency in neighboring regions.

Land Use/Land Cover Change and Their Driving Factors in the Yellow River Basin of Shandong Province Based on Google Earth Engine from 2000 to 2020

As the convenient outlet to the Bo Sea and the major region of economic development in the Yellow River Basin, Shandong Province in China has undergone large changes in land use/land cover (LULC) in the past two decades with rapid urbanization and population growth. The analysis of the LULC change patterns and its driving factors in the Shandong section of the Yellow River Basin can provide a scientific basis for rational planning and ecological protection of land resources in the Shandong section of the Yellow River Basin. In this manuscript, we analyzed the spatial pattern of LULC and its spatial and temporal changes in the Shandong section of the Yellow River Basin in 2000, 2010, and 2020 by using the random forest classification algorithm with the Google Earth Engine platform and multi-temporal Landsat TM/OLI data. The driving factors of LULC changes were also quantified by the factor detector and interaction detector in the geodetector. Results show that in the past two decades, the LULC types in the study area are mainly farmland and construction land, among which the proportion of farmland area has decreased and the proportion of construction land area has increased from 19.4% to 29.7%. Based on the results of factor detector, it can be concluded that elevation, slope, and soil type are the key factors affecting LULC change in the study area. The interaction between elevation and slope, slope and soil type, and temperature and precipitation has strong explanatory power for the spatial variation of LULC change in the study area. The research results can provide data support for ecological environmental protection, sustainable, and high-quality development of the Shandong section of the Yellow River Basin, and help local governments take corresponding measures to achieve coordinated and sustainable socioeconomic and environmental development.

Regional Disparities and Influencing Factors of Eco-Efficiency of Arable Land Utilization in China

Eco-efficiency of arable land utilization (EALU) emphasizes efficient coordination between land use systems and ecosystems. It is therefore of great significance for agricultural sustainability based on the systematic assessment of EALU. This study took carbon emissions and non-point source pollution resulting from arable land utilization into the measurement system of EALU, and a super-SBM model, kernel density estimation and Tobit regression model were used to analyze regional differences and influencing factors of EALU for 31 provinces in China from 2000 to 2019. The results showed that there was an upward trend in EALU in China from 0.4393 in 2000 to 0.8929 in 2019, with an average annual growth rate of 4.01%. At the regional level, the EALU of three categories of grain functional areas generally maintains an increasing trend, with the highest average value of EALU in main grain marketing areas (MGMAs), followed by grain producing and marketing balance areas (GPMBAs) and main grain producing areas (MGPAs). There are obvious differences in EALU among provinces, and the number of provinces with high eco-efficiency has increased significantly, showing a spatial distribution pattern of “block” clustering. In terms of dynamic evolution, kernel density curves reflect the evolution of EALU in China and grain functional areas with different degrees of polarization characteristics. The results of Tobit regression show that natural conditions, financial support for agriculture, science and technology inputs, level of industrialization, agricultural mechanization, and the living standards of farmers are significant factors resulting in regional disparities of EALU. Therefore, this study proposes the implementation of differentiated arable land use/agricultural management strategies to improve the sustainable utilization of arable land.

Characteristics and Influencing Factors on the Hollowing of Traditional Villages-Taking 2645 Villages from the Chinese Traditional Village Catalogue (Batch 5) as an Example

With the rapid development of urbanization and modernization, the population of traditional villages migrates into surrounding areas, causing the hollowing of traditional villages. The disintegration of China's traditional village means the loss of historical memory and cultural characteristics of ethnic regions, seriously endangering the country's cultural heritage. To better understand the hollowing phenomenon, this study analyzed 2645 villages from the Chinese traditional village catalogue (Batch 5) and evaluated different village attributes, including location, household registration, permanent population, number of traditional buildings, cultural relics, historical buildings, and non-heritage representative projects. We constructed an evaluation index system and used the entropy weight method, comprehensive evaluation method, and correlation analysis method to quantitatively assess the characteristics and influencing factors of hollowing among traditional Chinese villages. The main results are as follows: ① The hollowing index was above 0.5; most traditional villages have entered the stage of high hollowing. ② The traditional villages with hollowing index above 0.9 comprised 92%, and those between 0.8 and 0.9 made up 6%. Those with hollowing index at intervals 0.7-0.8, 0.6-0.7, and 0.5-0.6 accounted for 0.98%, 0.30%, and 0.11%, respectively. ③ Population hollowing is the fundamental cause of traditional village hollowing. In more than 99% of traditional villages, the population hollowing index was greater than 0.7. ④ More than 99% of traditional villages have a building hollowing index greater than 0.4, and more than 92% of the villages had a per capita number below 0.1. ⑤ The cultural hollowing rate for most traditional villages was very high. The cultural hollowing index for more than 99% of traditional villages was greater than 0.7. This study provides references for government administrators and scholars in rural revitalization and traditional village hollowing governance.

Linking DPSIR Model and Water Quality Indices to Achieve Sustainable Development Goals in Groundwater Resources

The achievement of sustainable development goals in groundwater resources related to water quality issues is a critical question in many regions. This study aims to combine powerful tools for helping stakeholders and policymakers achieve sustainable development goals in groundwater resources of agricultural areas. The DPSIR (Driver–Pressure–State–Impact–Response) model in combination with the Canadian Council of Ministers of Environment Water Quality Index and Groundwater Directive 2006/118/European Community—Threshold Values was applied using a hydrogeochemical dataset derived from the analysis of groundwater samples collected from 31 monitoring sites in an unconfined alluvial aquifer. Elevated Cl− (up to 423.2 mg L−1), NO3− (up to 180.1 mg L−1) concentration and electrical conductivity (up to 2037 μS cm−1) value are observed for groundwater samples of the study area. The outcome of the “One Out-All Out” procedure revealed that the groundwater in 42% of the monitored sites is unsuitable for drinking according to the health-based guideline values established by Directive 98/83/European Community. A difficulty to achieve targets under Sustainable Development Goals 3 and 6 in the study area is revealed. The proposed response actions are reported.