Regional Research on Ecological Environment in China: A Literature Review

Abstract

With the rapid development of China’s economy, resource consumption and environmental pollution have become challenges faced by China in its development process. In order to effectively achieve a balance between economic development and ecological and environmental protection, the Chinese government has successively introduced development strategies for ecological environment construction. However, how to scientifically evaluate the quality of regional ecological environments, analyze related impacts, and promote national ecological and environmental governance has always been difficult to reach consensus and continues to receive attention from the academic community. This paper sorts through research in recent years about regional ecological environment assessments in China in order to summarize the current assessment methods and dimensions of regional ecological environment research in China, as well as the impact of regional ecological environment construction. In terms of evaluation methods, this paper analyzes the applicability and limitations of current mainstream methods. In terms of evaluation dimensions, this paper summarizes the research results from different regional dimensions. In terms of the impact of regional ecological and environmental construction, this paper elaborates on the three aspects of influencing factors, influencing effects and research method analysis. Based on the above analysis, this paper finally proposes that the focus of future research should be on digital analysis and the evaluation of regional ecological and environmental quality, so as to provide more scientific and accurate support for regional ecological and environmental governance.

1. Introduction

Since the industrial revolution, human activities have significantly accelerated greenhouse gas emissions [1], biodiversity loss [2], and environmental pollution [3], reshaping the Earth’s ecosystems at an unprecedented pace. Environmental degradation has now emerged as one of the most pressing global challenges for sustainable development [4]. This environmental deterioration profoundly affects sustainable development through multiple pathways. From an economic perspective, the depletion of natural capital has heightened resource scarcity, forcing traditional industries to navigate between green transformation and market elimination [5]. Under the combined influence of stricter environmental regulations and fluctuating factor prices, the criteria for assessing industrial competitiveness have shifted from a sole focus on production efficiency to a more comprehensive evaluation of ecological efficiency [6]. On the social front, disparities in environmental pollution exposure have introduced a new dimension of inequality, as marginalized communities are often concentrated in ecologically vulnerable areas [7]. The resulting negative cycle of health deterioration and livelihood fragility is eroding the foundations of social equity. From an ecological perspective, the continuous decline in fundamental ecosystem services such as water cycle regulation and nutrient supply [8] has heightened systemic risks to food security and disaster resilience, posing significant threats to human survival [9]. This escalating crisis has prompted the United Nations to place environmental protection at the heart of its 2030 Agenda for Sustainable Development, underscoring the pivotal role of environmental governance in reshaping the global governance framework [10]. In response to the urgent global demand for enhanced environmental governance, China has integrated ecological civilization into its broader national governance modernization strategy. China’s “Ecological Civilization Construction” is a national strategy focused on sustainable development, aiming to harmonize economic growth with environmental protection [11]. It promotes green transformation by enforcing strict environmental regulations, controlling pollution, restoring ecosystems, and setting goals like carbon neutrality [12]. As the country enters a new phase of high-quality development, its environmental efforts have shifted from end-of-pipe treatment to proactive prevention at the source [13] and from isolated interventions to systematic governance approaches [14]. China has progressively established a comprehensive governance framework that spans territorial spatial planning [15], ecological compensation mechanisms [16], and environmental judicial systems [17].

Within this framework, China’s environmental governance emphasizes not only vertical advancements in key strategic dimensions, but also horizontal coordination aligned with regional functional roles, thereby establishing a multidimensional governance structure characterized by “deepening efforts across layers and implementing region-specific measures”. From the perspective of concern, the Chinese government has adopted a systematic approach that integrates efforts across core areas such as industrial transformation, financial innovation, and the energy revolution [18], constructing a multi-level environmental governance framework. At the industrial level, structural pollution problems associated with industrialization remain prominent, and energy-intensive industries such as steel and chemicals continue to face pressure for green transformation [19]. However, by promoting circular economy models and adopting cleaner production technologies, key industries are progressively exploring pathways for pollution and carbon reduction [20]. In the financial sector, the development of a green finance system has accelerated, with the carbon emissions trading market gradually expanding its coverage [21]. The integration of environmental risk assessments into credit evaluations by financial institutions is also advancing, injecting market-driven momentum into environmental governance. Regarding the energy sector, the sustained decline in coal consumption combined with the rapid expansion of renewable energy capacity has created a balancing dynamic [22]. Although the ecological benefits of clean energy substitution have yet to be fully realized, the transformative direction of the energy revolution has become increasingly evident. From a regional perspective, China’s environmental governance exhibits significant regional differentiation, adopting tailored solutions aligned with distinct regional strategic roles and ecological functions. For instance, the Beijing–Tianjin–Hebei region and its surroundings serve as the core area for China’s air pollution prevention efforts [23], with air quality directly influencing ecological security in the North China Plain and the well-being of hundreds of millions of residents. Longstanding issues such as the region’s heavy industrial structure and stable atmospheric conditions during autumn and winter have contributed to persistent smog problems [24]. In response, China has promoted comprehensive improvements in air quality through ultra-low emission retrofitting of coal-fired boilers, establishing cross-regional joint prevention and control mechanisms, and enforcing the dynamic elimination of high-pollution enterprises [25]. In the Yangtze River Delta region, which holds dual responsibilities as China’s economic engine and a critical hub for aquatic ecological security, long-term challenges such as declining aquatic ecosystem functions and nearshore marine pollution have constrained regional sustainable development [26]. By establishing a pollution source tracing and control system in the Taihu Basin and implementing ecological restoration projects in the Yangtze River Estuary, the region is actively exploring coordinated water governance solutions. In the western ecological barrier region, known as Asia’s “water tower” and a biodiversity stronghold, the risk of ecological degradation poses a threat to China’s ecological security baseline [27]. Efforts such as the establishment of a national park system, the implementation of alpine grassland restoration projects, and the deployment of glacier monitoring and early warning systems are reinforcing the region’s ecological barrier functions [28]. In the Guangdong–Hong Kong–Macao Greater Bay Area, a global supply chain hub, the interplay of land-based and marine pollution alongside cross-border environmental governance has historically posed significant challenges. By innovating cross-border environmental standards recognition mechanisms [29], advancing the “Blue Bay” restoration initiative in the Pearl River Estuary, and establishing an air pollution joint prevention and control platform [30], the Greater Bay Area is developing a new paradigm for open and cooperative environmental governance.

To further advance regional ecological environmental governance in China, conducting corresponding assessment studies is of significant importance, with theoretical and practical value demonstrated in three key areas: the systematic integration of methodologies, the construction of multidimensional evaluation frameworks, and the dynamic analysis of influencing factors and effects. First, systematically summarizing existing assessment methods is crucial for developing a scientific assessment toolkit and driving methodological innovation and iteration [31]. By clarifying the theoretical foundations, technical pathways, and applicable boundaries of different methods, methodological guidance can be provided for diversified assessment needs, while cross-disciplinary comparisons and validations promote method improvement, laying the groundwork for dynamic monitoring systems and standardized evaluation procedures [32]. Second, an in-depth analysis of the critical value of multidimensional evaluation frameworks aims to achieve spatial adaptation between governance levels and ecosystems [33]. By adopting a hierarchical structure, the evaluation framework can be applied at the national level [34], provincial dimension [35], city and city cluster dimension [36], and rural and county dimension [37], inherently forming an evaluation network that links “macro-strategic guidance—meso-level coordinated control—micro-level implementation feedback”, ensuring that ecological governance policies maintain top-down consistency while allowing flexibility in local practice [38]. Furthermore, exploring the influencing factors and resulting effects aims to establish an evidence-based decision-making loop for environmental governance [39]. Analyzing the mechanisms of various influencing factors and quantitatively assessing the effects of ecological governance can reveal causal pathways and key leverage points for regional ecological improvement [40], providing theoretical support for precise policy implementation and dynamic monitoring of governance outcomes. The coordinated advancement of these three aspects will foster the integration of evaluation theory, governance practice, and policy innovation, offering multi-scale and comprehensive decision-support tools for “Ecological Civilization Construction”.

Based on the significance of the above-mentioned regional ecological environment assessment studies, the remaining sections of this paper are structured as follows: Section 2 introduces the methods for regional ecological environment assessment in China, primarily summarizing the definitions, application scenarios, and improvements of these methods. Section 3 reviews the dimensions of regional ecological environment assessment, comparatively analyzing the importance of each dimension and the effectiveness of governance across dimensions, while identifying the relative shortcomings and comparative advantages of each dimension’s development. Section 4 focuses on the impacts of regional ecological environment construction, exploring the influencing factors during the construction process and the effects observed afterward, along with introducing relevant impact analysis methods. Section 5 presents the conclusion, summarizing the content of this paper, highlighting existing deficiencies, and proposing suggestions for improving China’s regional ecological environment assessment in the future. This review provides a scientific basis for evaluating environmental policy effectiveness, identifying environmental governance shortcomings, and serves as a reference for designing targeted ecological compensation policies, optimizing collaborative governance mechanisms, and advancing environmental assessment from single-point regulation to comprehensive performance analysis.

2. Methods for Regional Ecological Environment Assessment

2.1. Single Indicator Method

Within the methodological framework of regional ecological environment assessment, the Single Indicator Method serves as a fundamental evaluation tool with a well-established theoretical foundation. The ecological environment system is a complex and dynamic system [41] that encompasses both biotic and abiotic components, whose interactions are intricate and often nonlinear. Nevertheless, extensive research has revealed that despite the system’s inherent complexity, certain key indicators remain highly sensitive to environmental changes and can effectively capture core characteristics and major trends within the ecological environment system [42]. Consequently, the Single Indicator Method selects representative indicators [43] for quantitatively assessing the regional ecological environment conditions.

From the perspective of assessment targets, the Single Indicator Method is particularly suitable for studies with clear research objectives and relatively simple assessment subjects. For soil environment assessment, heavy metal content in soil is a crucial indicator [44]. Excessive levels of these heavy metals can impair soil fertility, hinder plant growth, and even enter the food chain, posing risks to human health [45]. In marine ecological environment assessment, the population of marine plankton is frequently adopted as a single indicator. Plankton plays a vital role in marine ecosystems, and changes in their population can indicate the overall health of the marine environment [46].

The advantage of the Single Indicator Method lies in its simplicity [47], which reduces analytical complexity and improves decision-making efficiency by quickly quantifying complex problems through a single dimension [48], and facilitates standardized comparison and implementation [49]. Its limitation lies in its one-sidedness, neglecting multi-dimensional correlation and leading to imbalance in assessment [50]. The static perspective is difficult to adapt to dynamic system changes and is susceptible to subjective bias, which may lead to incentive distortions.

With the advancement of academic research, improvements to the Single Indicator Method have primarily focused on indicator selection and calculation method optimization [51]. By integrating multiple factors, these improved indicators become more representative. For example, in calculating the comprehensive water quality index, incorporating ecological function indicators of water bodies [52] can enhance the comprehensiveness of assessment results and more accurately reflect water environment quality. Beyond traditional ecological function indicators, some emerging improvement methods are also under exploration. One such method involves leveraging machine learning algorithms to optimize indicator selection. In machine learning, feature selection algorithms can automatically filter key indicators [53]. LASSO regression compresses unimportant indicator coefficients to zero through L1 regularization and retains strongly correlated features [54]. Random Forest quantifies the indicator contribution through feature importance scores and excludes low-weight indicators. Recursive Feature Elimination combines with cross-validation to gradually exclude redundant indicators [55]. When implemented, Python 3.12’s Scikit-learn library provides LassoCV, RandomForestRegressor, and RFECV modules, which can be directly invoked, and integrated models such as XGBoost can also generate indicator rankings through the built-in feature importance analysis, which can be combined with visualization tools to quickly locate the core variables and improve the accuracy and efficiency of single-indicator methods. Another emerging method combines remote sensing data with the Single Indicator Method [56]. Remote sensing technology can provide real-time, large-scale ecological environment data, such as monitoring wetland area, forest health, and water body distribution via satellites [57].

2.2. Indicator System Method

Compared to the Single Indicator Method, Indicator System Method places greater emphasis on the comprehensiveness and systematization of the assessment [58]. The ecological environment, as a complex and integrated system, consists of multiple interconnected subsystems and elements. In such a complex ecosystem, a single indicator is sometimes insufficient to fully and accurately capture information across its various dimensions. Therefore, Indicator System Method provides a comprehensive tool for regional ecological environment assessment by constructing a series of interrelated and complementary indicators [59]. These indicators are not isolated but are combined in a way that reflects the intrinsic relationships between different ecological elements, offering a comprehensive representation of the overall state of the ecological environment.

For research that aims to explore regional ecological environmental issues in depth, Indicator System Method offers distinct advantages. For example, in studying the coordinated ecological development of urban clusters, constructing an indicator system that includes economic, social, and environmental dimensions helps relevant authorities analyze the interrelations and the degree of coordinated development of the ecological environments across cities within the cluster [60]. In the economic dimension, indicators such as GDP and the greening degree of industrial structure reflect the impact of economic development on the ecological environment [61]. The social dimension includes indicators like population density and public environmental awareness, revealing how social factors influence the ecological environment [62]; the environmental dimension encompasses various environmental quality and ecosystem indicators, providing a comprehensive view of the urban ecological environment [63].

The advantages of the Indicator System Method lie in its comprehensiveness, avoiding one-sided assessments by integrating complex environmental, economic, and social linkages through multidimensional indicators [64]; the structured framework supports dynamic monitoring and system optimization, enhancing the depth of scientific decision-making [65]. Its limitations are the high complexity of construction and the high cost of data collection and standardization [66]. There may be redundancies or contradictions among indicators, and the allocation of weights is susceptible to subjective interference; excessive refinement may weaken operability, increase the difficulty of interpretation and application, and affect the efficiency of actual policy implementation [67].

To improve the scientific rigor and effectiveness of Indicator System Method, recent research has made significant strides in indicator selection and weight allocation. Common methods for indicator selection include principal component analysis and analytic hierarchy process. Principal component analysis reduces dimensionality by combining strongly correlated original indicators into a smaller set of principal components, preserving most of the original information while reducing redundancy, thereby enhancing the effectiveness of the indicator system [68]. Analytic hierarchy process decomposes complex problems into hierarchical levels and determines the relative importance of each level through pairwise comparisons, facilitating the selection of the most representative indicators [69]. In terms of determining weights, common methods include entropy weight method and fuzzy comprehensive evaluation method. The entropy weight method assigns weights based on the variability of the indicator data, with higher weights given to indicators with greater variability, ensuring a more objective and reasonable weight distribution [70]. Fuzzy comprehensive evaluation method constructs a fuzzy relationship matrix, considering the combined influence of multiple factors to handle the fuzziness in assessments and determine weights.

Furthermore, the traditional Indicator System Method relies on manual experience or static calculation for weight allocation, but machine learning can be improved to a dynamic data-driven model [71]. The gradient boosting tree model automatically analyzes the contribution of each indicator to the goal through training and generates importance scores as the basis for weighting [72]. The attention mechanism model captures the complex correlation between indicators and dynamically allocates weights through the self-attention layer [73]. The neural network combines backpropagation to optimize the weight parameters and adapt to changes in data distribution [74]. For implementation, the XGBoost library in Python can be used to directly extract feature importance, and TensorFlow 2.16.1 or PyTorch 2.4 can be used to build the attention network model.

2.3. Data Envelopment Analysis Method

Data envelopment analysis (DEA) is a non-parametric analysis tool based on linear programming, which has demonstrated its unique value in the field of regional ecological environment assessment [75]. By constructing a linear programming model, DEA effectively measures the relative performance of regions in terms of resource utilization and output in complex scenarios with multiple inputs and outputs, providing a new and effective analytical perspective for regional ecological environment assessments.

DEA can be classified into three types: input-oriented, output-oriented, and non-oriented. Input-oriented DEA focuses on exploring how to reduce inputs while maintaining output levels [76]. In contrast, output-oriented DEA emphasizes how to increase outputs while keeping inputs constant [77]. Non-oriented DEA focuses on achieving overall system efficiency improvements within a framework that optimizes both inputs and outputs simultaneously [78]. This model overcomes the limitations of single-dimensional optimization, constructing a synergistic improvement path for reducing inputs and increasing outputs, which is suitable for complex systems requiring a balance between resource constraints and development goals [79].

The advantage of the data envelopment analysis method lies in its nonparametric characteristics [80], which can handle multi-input and multi-output systems without relying on a predefined model, and its ability to objectively assess the relative efficiency of decision-making units through data-driven optimization weights. The method can flexibly adapt to different scale gain assumptions, supports cross-unit efficiency comparison [81], and is suitable for efficiency analysis scenarios of complex systems. However, its limitations are also more prominent [82], which are manifested in the extremely high data quality requirements, the fact that the outliers may significantly distort the efficiency frontier, and the lack of statistical testing mechanism, which makes it difficult to quantify the reliability of the results [83].

To overcome the limitations of traditional DEA models, researchers have introduced several innovative methods. The super-efficiency DEA model addresses the issue in traditional DEA models where when multiple decision-making units are located on the efficiency frontier, further comparisons are not possible [84]. In traditional DEA models, when multiple regions are rated as being on the efficiency frontier, it is difficult to identify subtle differences between them. However, the super-efficient DEA model is able to further rank these efficient decision-making units, providing more precise assessment results [85]. In terms of frontier improvement methods, the Slacks-Based Measure (SBM) model has also been widely applied [86]. Traditional DEA models typically assume radial and angular invariance, which may overlook slack variables between inputs and outputs. The SBM model more effectively handles undesirable outputs (such as pollutant emissions), allowing for a more comprehensive assessment of regional ecological environment efficiency and avoiding evaluation errors caused by model assumption biases [87]. Furthermore, the network DEA model has gradually become a research hotspot. This model treats the internal production process of decision-making units as a network structure, considering the interrelationships between subprocesses, enabling a deeper analysis of the efficiency of different components in the regional ecological environment system and providing a basis for optimizing specific links in ecological environment governance [88].

2.4. Other Methods

The ecological footprint method, based on the principles of material and energy balance in ecosystems, is an important analytical tool in regional ecological environment assessments. This method quantifies the demand of human activities on ecosystems by precisely calculating the ecological productive land area required to support human production and life [89]. As research deepens, the ecological footprint method continues to improve. For instance, the classification of ecological productive land has been further refined by subdividing forests into primary forests, artificial forests, and economic forests, enhancing the accuracy of calculations [90]. In terms of optimizing calculation models, model parameters are adjusted based on the region’s natural geographical features and climatic conditions to improve the precision of assessment results [91].

Ecosystem services value assessment aims to provide a comprehensive monetary valuation of the various services provided by ecosystems [92]. In regional ecological environment assessments, drawing on the approach of Wilkinson and Gerth [93] in helium resource sustainability assessment, ecosystem services value assessment can provide economic support for ecological protection and rational resource utilization. Currently, the improvement of ecosystem services value assessment is mainly focused on optimizing the assessment model to better reflect the actual functions and service values of ecosystems [94]. This may involve adjusting the model structure to account for the interactions between different ecosystem services or improving the accuracy and comparability of assessment results through field surveys and data collection for parameter selection.

Spatial analysis methods, utilizing advanced technologies such as Geographic Information Systems (GIS) and Remote Sensing (RS), analyze regional ecological environment data based on the principles of spatial correlation and heterogeneity, and visualize the results [95] to precisely reveal the spatial distribution of ecological environmental factors and the evolution patterns of ecological issues [96]. With continuous technological advancement, new analysis models and algorithms are being incorporated into spatial analysis methods, such as geostatistical analysis and spatial autocorrelation analysis. Geostatistical analysis can predict the spatial distribution trends of ecological environmental factors [97], while spatial autocorrelation analysis helps identify spatial clustering or dispersion patterns of ecological environmental factors [98], further enhancing the precision and depth of spatial data analysis.

3. Regional Ecological Environment Assessment in Spatial Dimensions

Region refers to the area mainly referring to the country and the secondary spatial unit within the country, covering a multi-level spatial structure from macro to meso to micro [99]. Regions can take multiple countries as research objects, such as the BRICS countries, the Belt and Road countries, and the Commonwealth countries, or administrative areas within a country, such as the states in the United States or the provinces, cities, and villages in China. Focusing on the different scopes of regional definition, this section explains the current status of regional ecological environment research in different spatial scopes.

3.1. National Dimension

Broadly speaking, the concept of a region can refer to specific areas within a country or encompass one or more countries. Compared to regional ecological environment assessments at the provincial or city level, assessing ecological environments from the perspective of one or more countries allows for a more focused evaluation of ecological environment quality, the current state of ecological development, and the implementation of policies aimed at promoting ecological progress within national borders [100]. Since national-level ecological assessments involve complex cross-regional environmental processes, the impact of global climate change on domestic ecosystems and the influence of the global environmental governance system on national environmental policies deeply affect the domestic ecological governance [101]. Therefore, research on ecological environment quality at the national level is crucial for formulating national ecological protection strategies, optimizing ecological governance systems, and fostering international cooperation [102]. Current national-level ecological assessments primarily focus on longitudinal studies of a single country over time and cross-country comparisons based on the same indicators.

In terms of vertical comparisons of regional ecological environments at the national level, existing research often focuses on comparing the ecological environment changes at different periods within China, thus revealing the effects of ecological policies issued by the Chinese government. The achievements of national environmental governance at each stage can be evaluated using key environmental indicators such as carbon emission levels [103], air quality index (AQI) [104], and forest vegetation coverage rate [105]. These results help illustrate the relationship between national economic development, policy evolution, and ecological environment quality. In the early stages of China’s reform and opening-up, rapid economic growth was accompanied by a significant decline in ecological environment quality. Existing studies have shown that through comparisons of air pollutant concentrations and water pollutant emissions, pollutant emissions continuously rose, and ecological environment quality deteriorated at the same time [106]. However, in the 21st century, with increased government efforts on environmental governance, the growth rate of major pollutant emissions has slowed down, the air quality has improved, and the vegetation coverage has increased in some regions [107]. Nevertheless, some researchers point out that despite overall improvements in ecological environment quality, certain urban areas still face issues such as ecosystem degradation and soil pollution [108]. Overall, the development of China’s ecological environment quality shows a “deterioration followed by improvement” trend over time [109], with future development still requiring policy optimization and technological innovation to achieve national ecological governance and improvements.

In horizontal comparisons of regional ecological environments at the national level, scholars often compare China’s ecological environment development with other countries to analyze China’s relative shortcomings and comparative advantages in the ecological governance process. Current research mainly compares core indicators such as carbon emissions and water resource utilization between different countries to comprehensively assess the differences and similarities in ecological governance. From the perspective of carbon emission intensity, the comparative study between China and developed countries such as some members of the European Union and the United States shows that although China has the highest total carbon emission in the world, the total carbon emission per capita in China has shown a downward trend in recent years with the implementation of energy conservation and emission reduction policies [110]. However, due to China’s energy structure still being dominated by coal, there is still a large gap in the proportion of low-carbon energy compared with developed countries [111]. In terms of water resource utilization, studies comparing China with the USA have found that although China has implemented water pollution prevention and control action plans, it still faces severe water shortages, with its per capita water resources being only one-quarter of those in the USA [112]. In terms of air quality, China has significantly reduced PM2.5 concentrations in recent years, with its rate of environmental improvement far surpassing that of other emerging economies like India [113]. In contrast, air pollution in major Indian cities is still worsening, and the effectiveness of the country’s ecological environmental protection policies remains limited. In general, compared with some developing countries, the policies implemented by the Chinese government to improve the quality of ecological environment have achieved certain governance results in recent years, but there is still a big gap compared with developed countries [114], which needs to be narrowed by some specific measures in the future.

In the regional ecological environment assessment at the national level, scholars’ research mostly focuses on the macro-level policy implementation effect test and the changing trend of environmental quality. The research and analysis are highly systematic and directional. At the same time, when conducting analysis, researchers highlight the achievements and shortcomings of China in ecological environment governance by comparing the ecological environment quality with other countries. This type of research also helps to grasp the overall trend of China’s ecological governance and provide empirical references for the country’s ecological environment governance. However, when comparing the ecological environment quality between countries, researchers rarely pay attention to the potential deep-level influencing factors such as the country’s institutional background and cultural beliefs, which makes the conclusion lack of explanatory depth.

3.2. Provincial Dimension

In assessing the ecological environmental quality at the provincial level, scholars comprehensively consider differences in resource utilization, industrial structure, environmental protection measures, and social benefits across provinces. Therefore, research on ecological environmental quality from the provincial perspective tends to focus more on the practical effects of local environmental governance, especially the differences in industrial upgrading [115], resource utilization efficiency [116], and environmental policy implementation [117] during the development process of provinces. Due to China’s vast territory and the diversity of provinces, each with distinct ecological characteristics [118], studies from the provincial level can provide specific improvement paths for local ecological environmental quality and also offer strong evidence for optimizing national policies [119]. Additionally, the relatively independent administrative management of provincial units facilitates data collection and operational feasibility for scholars conducting research at this level [120]. Currently, ecological environment assessments at the provincial level mainly focus on longitudinal comparisons of a single province over time and horizontal comparisons of different provinces based on the same indicators.

In terms of longitudinal comparisons of provincial ecological environments, existing research predominantly emphasizes the comparison and analysis of ecological environment changes in a single province over different periods, in order to further validate the impacts of economic development, industrial restructuring, and environmental policy implementation on ecological environment quality. Scholars rely on indicators such as resource utilization, pollutant emissions, and air quality in each province to reveal the ecological environment quality characteristics of the same province at different times [121]. In terms of resource utilization, through observing the energy consumption of various provinces, with the promulgation of the “dual carbon” target and the “14th Five-Year Plan” and other policies, provinces actively respond to the requirements of energy conservation and emission reduction and accelerate the pace of industrial structure upgrading in their own provinces. As a result, the energy utilization efficiency of all provinces in China has been improved as a whole compared with the past ten years [122]. According to data from the National Bureau of Statistics of China, between 2022 and 2024, Beijing, Shanghai, Zhejiang, and other places will continue to promote the intensive use of land resources. In 2023, the GDP output per unit of construction land in Beijing is expected to reach 410 million yuan per hectare, an increase of about 12.3% from 2021, further reflecting the continuous optimization of resource utilization efficiency [123]. Regarding changes in pollutant emissions, during the early 21st century, the industrial pollution control efforts of many provinces were limited, leading to high levels of wastewater, waste gas, and solid waste emissions at that time [124], placing the ecological environment quality of many provinces at a relatively low level. However, with the introduction of binding emission reduction targets in the “11th Five-Year Plan” and the deep promotion of the “carbon peak” policy, pollution prevention in various provinces has become more refined, resulting in a downward trend in pollutant emissions [125]. Regarding air quality, from the late 20th century to the early 21st century, smoggy weather was frequent across the country, and air pollution became a social focus at that time [126]. In response, provinces took measures such as coal combustion control and the promotion of clean energy, leading to continuous improvements in air quality and a significant reduction in the frequency of pollution-related weather events [127].

Regarding horizontal comparisons of provincial ecological environments at the provincial level, scholars compare the ecological environment quality of different types of provinces to reveal how economic development, industrial restructuring, and resource endowment differences influence environmental governance, thus further achieving tailored regional ecological environment management policies for each province. Current research primarily compares the ecological environment quality of provinces based on several aspects, such as ecosystem services, biodiversity, and carbon emission intensity. In terms of ecosystem services, the ecosystem services of a province directly reflect the function, state, and capacity of the region’s ecological environment, influencing the sustainability of natural resources and ecological security in the province [128]. Scholars have found that southern provinces, which are rich in ecological resources, perform significantly better than northern provinces, which are ecologically more fragile [129]. Regarding biodiversity comparisons, economically developed provinces have suffered from urbanization and industrial development, leading to the destruction of some original ecosystems, and their biodiversity levels are lower compared to provinces with better natural environment protection [130]. In terms of carbon emission intensity, scholars typically compare per capita carbon emissions across provinces and have found that carbon emission intensity remains high in some resource-dependent provinces, which are unlikely to reduce their emission intensity in the short term [131]. Meanwhile, some eastern coastal provinces have seen a faster decline in carbon emission intensity, mainly due to industrial upgrading and the application of green technologies [132]. At the same time, according to data from the China Carbon Accounting Database (CEADs) and reports released by Greenpeace, the carbon emission intensity of Hebei Province is about 2 tons of CO₂/10,000 yuan of GDP, while the carbon emission intensity of Zhejiang, a coastal province, is about 0.9 tons of CO₂/10,000 yuan of GDP. From this data comparison, it can also be seen that the carbon emission intensity of provinces in traditional industrial areas is higher than that of provinces in the eastern coastal areas [133]. In summary, the ecological environment quality in different regions of China shows distinct characteristics [134], and provinces are expected to form more coordinated development in environmental governance, thereby providing stronger support for the improvement of national regional ecological environment quality.

Regarding the regional ecological environment assessment at the provincial level, the current research of scholars can reflect the specific performance of each province in terms of resource utilization, industrial structure, and policy implementation. With the help of the databases of various provinces, the current research can accurately show the evolution trend and current situation of the provincial environmental quality. At the same time, it can also reflect the differences in ecological environment quality caused by different resource endowments, development stages, and governance capabilities among different provinces through horizontal comparison. However, the current research on ecological environment quality at the provincial level tends to be quantitative research, lacking a systematic analysis of soft factors such as institutional execution and social participation.

3.3. Urban or Urban Cluster Dimension

With the ongoing acceleration and development of urbanization in China, cities serve as core units for economic, social, and cultural activities [135], and their ecological environment quality directly impacts the ecological development of both regions and the nation. Compared to national-level or provincial-level ecological assessments, studies at the urban level typically emphasize resource efficiency, environmental carrying capacity, and ecological governance capabilities [136]. Therefore, research on ecological environment quality at the urban level provides valuable insights into the effects of policies and human activities on the environment, which is crucial for devising more refined urban ecological governance strategies and optimizing regional development [137]. Currently, urban-level ecological assessments are mainly embodied in the longitudinal comparison study of a single city based on the time dimension and the horizontal comparison study of different cities based on the same indicator system.

In longitudinal comparisons of urban-level ecological environments, scholars assess changes in energy efficiency, air quality, and land use over different time periods. In terms of energy efficiency, by comparing per capita energy consumption and the proportion of clean energy used, studies show that major cities had low energy efficiency at the beginning of the 21st century [138]. However, following the implementation of energy-saving policies and the widespread use of clean energy, per capita energy consumption has decreased, while the proportion of clean energy has increased [139]. Regarding air quality, the implementation of the 2013 Air Pollution Prevention and Control Plan has led to a decrease in the annual average concentration of PM2.5 in cities over the past two decades [140]. As for land use, China’s cities have experienced significant changes, transitioning from extensive to more intensive land use practices, leading to improved land use efficiency over the past 20 years [141]. In addition, the implementation of smart cities, low-carbon pilot cities and innovative city policies has also brought about changes in the quality of urban ecological environment. Specifically, since 2008, the smart city pilot has promoted the application of information technology in urban governance. Through intelligent energy systems and environmental monitoring platforms, major pilot cities have further promoted the progress of urban energy conservation and emission reduction [142]. In 2010, the Chinese government officially launched the low-carbon pilot city policy. Studies have shown that the energy efficiency and air quality of such pilot cities have been greatly improved compared with the past [143]. As for the innovative city policy, its implementation emphasizes the combination of scientific and technological innovation and green development. Therefore, the industrial structure of cities implementing this pilot policy has been optimized and changed, and the quality of the urban ecological environment has also been significantly improved [144].

In horizontal comparisons of urban ecological environments, existing research compares green space coverage, ecological footprints, and industrial transformation across different cities [145]. This helps cities identify weaknesses in ecological governance and develop feasible targets. In studies comparing green space coverage, southern cities tend to focus more on ecological tourism and green space development [146], resulting in higher green coverage compared to northern cities [147]. In ecological footprint comparisons, cities with lower population density tend to have higher proportions of low-energy and green industries, resulting in lower ecological footprints, while cities with higher population densities show higher ecological footprints due to greater resource consumption [148]. Regarding industrial structure transformation, cities with developed high-tech sectors tend to have greener industrial transformations and higher ecological quality than cities focused on heavy industries [149]. Overall, cities with different characteristics exhibit varying levels of ecological quality, driven by differences in resources and economic development [150]. Northern and resource-based cities still face significant challenges in ecological governance [151] and need further industrial transformation and improved resource efficiency to enhance ecological quality.

As regional integration progresses, urban clusters are becoming increasingly important in China’s economic and social development. Compared to individual cities, urban clusters have more systemic and interconnected resource flows, industrial collaboration, and ecological governance [152]. Therefore, when conducting ecological assessments at the urban cluster level, it is essential to consider not only the ecological quality of each city but also the mutual influence between cities [153], including cross-regional pollution transmission, ecological compensation mechanisms, and regional governance improvements. In longitudinal assessments, changes in ecological quality within an urban cluster occur as a result of adjustments in industrial policies [154]. Studies monitoring air quality index changes over time within urban clusters show that clusters dominated by high-pollution industries have poorer air quality, while clusters focused on high-tech and modern services perform better [155]. In horizontal comparisons, development intensity and population density directly affect ecological carrying capacity. For example, the Pearl River Delta urban cluster, with its high-density urban form, has a significant urban heat island effect, while the Chengdu–Chongqing cluster, with more dispersed urban expansion and higher rural–urban land integration, has better ecological carrying capacity [156].

In the study of the city-level dimension, scholars can deeply reveal the ecological and environmental changes in specific cities driven by policies. Such research provides a specific reference for refined urban ecological governance policies. However, scholars pay too much attention to the changes in a single indicator when conducting research in this dimension, and then ignore the differences brought about by the original conditions between cities, which leads to the underestimation and neglect of urban environmental problems. In the ecological and environmental assessment at the city agglomeration level, scholars can focus on the synergistic effects and mutual influences between cities in the city agglomeration. Vertical and horizontal analysis can also reveal the differences in ecological carrying capacity and the laws of ecological and environmental changes in different city agglomerations. However, the current ecological and environmental research at the city agglomeration level has not fully taken into account the specific differences between different city agglomerations, such as the differences brought about by the economic development model and population density of different city agglomerations, which makes the current research in this dimension still have certain limitations.

3.4. Rural and County Dimension

With the implementation of the rural revitalization strategy, achieving the dual goals of ecological protection and economic development in rural areas has become a key focus of current research [157]. Rural areas and counties, as fundamental units of regional environmental governance, play a crucial role in promoting sustainable development, optimizing resource allocation, and improving residents’ quality of life [158]. In contrast to national and provincial ecological assessments, ecological evaluations at the rural and county levels are inherently complex and multi-dimensional. Scholars often combine ecological, economic, and socio-cultural factors to conduct comprehensive assessments based on various indicators. Research at this level emphasizes the coordinated development of humans and nature [159], particularly focusing on agricultural practices, resource efficiency, rural living conditions, and the balance between economic growth and ecological health. These assessments provide valuable insights into the dynamic changes in local environments, which are important for guiding rural ecological development [160]. Currently, rural and county-level ecological assessments mainly consist of longitudinal studies of individual rural areas over time and horizontal comparative studies between different rural areas using the same indicators.

In longitudinal studies, scholars typically analyze changes in ecological quality by focusing on shifts in agricultural practices and resource utilization. From the 1980s to the 1990s, rural agricultural production largely depended on traditional methods that heavily relied on chemical inputs such as pesticides and fertilizers. This led to significant degradation of soil and water quality in agricultural ecosystems [161]. With the advancement of agricultural modernization and the promotion of sustainable practices, as well as government policies encouraging green and ecological agriculture, rural agricultural practices have increasingly shifted toward resource-efficient and environmentally friendly methods. This transition has significantly reduced chemical input use and resulted in noticeable improvements in ecological quality [162]. Regarding resource utilization, rural areas in early 20th-century China exhibited relatively low efficiency, especially in water and land resources, with widespread overexploitation and waste [163]. However, with the adjustment of rural economic structures and the introduction of national environmental protection policies, resource efficiency has improved significantly, leading to a reduction in the environmental burden of agriculture [164].

In horizontal comparative analyses, scholars often focus on comparing pollution control efforts and the balance between economic development and ecological health across different rural counties in China. These studies aim to identify areas of strength and weakness in rural environmental governance. In terms of wastewater management, comparisons between counties with a high concentration of ecological demonstration villages and those in less developed regions reveal that the former lead in wastewater treatment infrastructure and agricultural non-point source pollution control, resulting in better water quality [165]. In terms of economic development and ecological coordination, rural counties in eastern coastal areas have pioneered the integration of ecological industries, leveraging their strong ecological environments to promote eco-tourism and green manufacturing, thereby achieving balanced economic growth and environmental protection [166]. In contrast, rural counties in central and western China still rely heavily on traditional agriculture and resource-based industries. Although some progress has been made in eco-tourism and green agriculture, the region still faces challenges due to weaker infrastructure and a high dependency on natural resources. The scale and efficiency of ecological economic development in these areas remain limited [167]. Horizontal comparisons highlight that rural counties in eastern China generally exhibit better ecological quality than those in the central and western regions. To improve ecological quality nationwide, rural counties in these regions need to further promote green agricultural practices, strengthen infrastructure, and address regional disparities in ecological environmental quality.

In the study of ecological environment quality at the rural and county levels, scholars were able to combine macro policy goals with micro ecological environment changes, which can well reflect the effectiveness of policy implementation in improving ecological environment quality. However, there are still certain limitations in the study of rural and county levels. This is specifically reflected in the fact that most of the current research is reflected in typical areas such as ecological demonstration villages or villages in developed eastern regions. The research samples are not representative enough and cannot fully reflect the situation of rural ecological environment quality.

3.5. Other Dimensions

In addition to traditional regional divisions, scholars also consider other specialized dimensions when conducting regional ecological environment assessments. These dimensions include ecological function zones, pilot cities, and watershed scales. Such dimensions often span multiple administrative units, focusing more on the integrity of ecosystems, natural resource carrying capacity, and the effectiveness of policy experiments. They place greater emphasis on the dynamic changes in ecological environments within specific spatial patterns and their impact on sustainable development [168]. By incorporating these multiple dimensions into ecological assessments, the theoretical framework of environmental evaluation is expanded, providing more detailed solutions for environmental governance in practice. This approach helps policymakers make more scientifically informed and precise decisions when addressing environmental challenges [169].

With China’s rapid socio-economic development and increasing land development intensity, ecosystems such as forests and wetlands have suffered significant degradation, leading to a loss of biodiversity [170]. Given China’s vast territory and the diversity of its ecological environments, the Chinese State Council introduced the ecological function zone strategy [171]. This strategy outlines areas within which specific ecological functions are to be maintained, playing a critical role in national and regional ecological security. Research on ecological function zones has shown that their establishment can effectively alleviate urban heat island effects, improve air quality, and enhance the quality of life for urban residents. However, with population growth and urbanization, some ecological function zones face significant pressure on their green spaces and air quality, requiring further intervention [172]. This indicates that the development of ecological function zones will continue to need substantial government support and investment.

Watersheds, as primary carriers of water resources, are composed of multiple ecosystems, including mountains, forests, wetlands, agricultural land, and water bodies, all contributing to a variety of natural resources and environmental functions [173]. Understanding the water resource status within a watershed is crucial for effective water management and conservation. In many regions, water pollution and ecological degradation are the result of overdevelopment and poor management within the watershed. Research on this dimension of ecological environmental quality often focuses on changes in water quality across different regions. For example, studies have shown that the water quality in the upper and middle reaches of the Yangtze River is relatively good, while the water quality in the downstream, more economically developed areas, is much poorer [174]. This disparity arises from strict forest protection policies and agricultural adjustments in the upper and middle reaches, while downstream areas are dominated by heavy industries and high-pollution sectors, leading to severe water pollution. This further underscores the importance of enhancing cross-watershed ecological compensation and collaborative governance mechanisms.

In the study of ecological environment quality in other dimensions, current scholars can start with special spatial dimensions such as ecological environment functional zones and watersheds. These dimensions of research break the limitations of traditional administrative divisions and can better reflect the interactive relationship between ecological processes and spatial patterns. However, there are still shortcomings in the research of scholars, which is specifically reflected in the fact that scholars still face great difficulties in data collection and standard coordination in other dimensions. Most current studies lack unified data support and long-term monitoring.

4. Exploration of the Impact on Regional Ecological Environment Construction

4.1. Analysis of Influencing Factors

Existing research has shown that numerous factors play a role in promoting regional ecological environment construction in China, encompassing both tangible material factors and intangible institutional and cultural factors. Tangible factors such as infrastructure development and regional economic growth are crucial drivers of ecological improvements. Well-developed environmental protection infrastructure enhances the capacity for collecting, processing, and disposing of pollutants [175], while green transportation infrastructure significantly reduces emissions [176]. The combination of these factors effectively mitigates the environmental impact of pollutants. Economic growth also increases fiscal revenue, which enables local governments and businesses to invest more resources in the development of pollution control technologies and the implementation of ecological restoration projects [177]. Furthermore, it supports the establishment of carbon emission trading markets [178], advancing the marketization and professionalization of environmental protection and incentivizing enterprises to reduce emissions voluntarily [179]. Other positive influencing factors include high vegetation coverage [180], advanced ecological farming models [181], population structure optimization, and labor force transformation [182], all of which also have positive effects on the regional ecological environment. Intangible factors, primarily related to policies, technological advancement, and public awareness, also play a significant role [183]. First, government policy guidance and institutional arrangements are vital for promoting ecological improvement [184]. In the “14th Five-Year Plan for Ecological and Environmental Protection”, China introduced stricter environmental protection assessment mechanisms and green development goals, driving localized environmental governance [185]. For example, the Beijing–Tianjin–Hebei region has significantly reduced PM2.5 concentrations [186] and improved regional air quality through targeted air pollution control actions [187]. Second, technological progress and innovation are critical in advancing ecological environment construction [188]. Recent breakthroughs in energy conservation, emission reduction, new energy development, and pollution prevention have gradually shifted China’s environmental governance from traditional end-of-pipe treatment to a more comprehensive management approach [189], leading to a significant reduction in pollutant emissions. The application of digital and information technologies in environmental protection has also improved the efficiency of pollutant source monitoring [190], early warning, and management, providing strong technical support for ecological environment protection [191]. Additionally, the increasing public awareness of environmental issues and the improvement of social oversight mechanisms have played a positive role [192]. With rising public concern about environmental problems, environmental NGOs [193], media [194], and social organizations are actively participating in environmental protection efforts [195], prompting governments and businesses to fulfill their environmental responsibilities and fundamentally improving the region’s ecological environment quality.

However, certain factors also inhibit ecological environment construction at the regional level in China. Existing literature mainly analyzes this from two perspectives: the relative insufficiency of positive factors and the occurrence and accumulation of negative factors. Positive factors such as funding [196], technological support [197], and the establishment of regional ecological environment evaluation systems [198] theoretically contribute to environmental improvements. However, in practice, insufficient support or incomplete system development often renders these factors constraints on ecological environment construction [199]. For example, in economically underdeveloped regions, a lack of environmental protection funding leads to an inability to meet the financial demands of large-scale environmental protection projects [200]. In some areas, technical support for environmental protection projects is also inadequate, particularly in central and western regions, where there is a lack of advanced environmental protection technology and management experience, resulting in suboptimal implementation of these projects [201]. Additionally, some areas have overly simplistic ecological environment evaluation systems that fail to comprehensively assess ecosystem health and ecosystem services [202]. This has resulted in undetected ecological risks, poorly informed policy formulation, and challenges in monitoring the effectiveness of environmental protection measures [203], ultimately affecting the execution and impact of policies. The accumulation and occurrence of negative factors primarily stem from inadequate government and departmental oversight [204], including rapid urbanization and industrialization [205], as well as illegal pollution discharge by enterprises [206]. Due to a lack of scientific planning and effective management, some regions have encountered a series of environmental problems, including ecological space compression, land degradation, and water pollution, during the urbanization and industrialization process [207]. Ecological buffers such as green spaces and wetlands have been over-exploited, disrupting ecosystem balance and reducing the natural self-cleaning ability of the environment [208]. Furthermore, some enterprises engage in environmental violations in pursuit of economic benefits, disregarding environmental protection laws to cut production costs [209]. A study collected 2,250 administrative penalties imposed on Chinese listed companies for environmental violations between 2015 and 2020, involving 924 companies, with cumulative fines amounting to RMB 543.7 million [210]. This behavior has caused significant environmental damage, posing a major obstacle to regional ecological environment construction.

Existing studies have systematically analyzed the influencing factors of regional ecological environment construction, comprehensively explored the mechanism of facilitating factors, and deeply analyzed the constraining paths of inhibiting factors, forming a relatively complete theoretical framework. However, there are still some limitations in the study: first, the lack of dynamic analysis of the interaction between facilitating and inhibiting factors has weakened the explanatory power of the complex system; second, there is insufficient attention to the regional differences, especially as the solution to the inhibiting factors of the underdeveloped regions mostly stays at the theoretical level and lacks targeted empirical evidence support; and third, the research methodology is biased in favor of quantitative analysis, and there is insufficient qualitative research on the intangible factors of the institutional culture and public participation, resulting in the lack of non-economic factors and the lack of empirical evidence support. In the future, it is advisable to strengthen the analysis of multi-factor coupling, deepen the differentiated research by combining regional characteristics, and integrate qualitative and quantitative methods to enhance the practical value of theoretical guidance.

4.2. Analysis of Influencing Effects

Ecological environment construction at the regional level in China has generated numerous positive impacts on the socio-economic system [211], public health, and ecosystems. The existing literature has analyzed these positive effects from two perspectives: direct and indirect impacts. Direct positive impacts mainly include attracting investment and improving regional social welfare [212]. Regions with excellent ecological environments are more likely to attract environmental protection organizations and enterprises for cooperation compared to areas with severe pollution [213]. This provides a platform for green investments and sustainable development projects [214], helping to enhance the region’s position within the national industrial chain [215]. For example, Anji County in Zhejiang Province has been promoting ecological restoration in the whole region since 2005 [216], raising the forest coverage rate to 70.1% and attracting green industry investment of about 18 billion yuan between 2021 and 2023, and has built ecological cooperation platforms such as the Zero-Carbon Bamboo Industrial Park; Xiongan New Area has invested a cumulative total of 28 billion yuan in the treatment of Baiyangdian since 2017 [217], pushing the water quality to reach the Class III standard, and subsequently attracting enterprises such as the China Energy Conservation Group to green investment more than 100 billion yuan, landing smart eco-city and low-carbon data center projects. Additionally, ecological environment construction has contributed significantly to ecological protection and restoration projects, resulting in the restoration of pristine natural landscapes and ecological resources in many areas [218]. A study analyzed the impact of urban green spaces on air pollution and residents’ health in 31 provinces in China. The results show that urban green spaces not only improve air quality, but also significantly enhance the health of residents, especially in the southern region [219]. This has provided local residents with cleaner air, purer water, and more livable environments [220]. These improvements not only enhance the quality of life for residents but also foster the development of emerging industries such as eco-tourism and the health industry [221], further boosting regional economic vitality [68]. Indirect positive impacts primarily focus on enhancing the stability of regional socio-economic systems and promoting the establishment of new management frameworks. Ecological environmental construction improves the stability of regional ecosystems by optimizing resource utilization efficiency and improving environmental quality [222], and further reduces the economic losses caused by natural disasters and environmental emergencies [223]. More stable environmental conditions lay a foundation for sustainable economic development by reducing the economic risks caused by environmental degradation [224]. Furthermore, governments at various levels have gradually established more transparent and efficient governance models while promoting the implementation of environmental protection policies [225]. By enhancing environmental information disclosure and normalizing environmental law enforcement, public participation in environmental protection has been significantly boosted [226], leading to a new governance model of multi-stakeholder collaboration [227]. On this basis, the further improvement of urban infrastructure and public services has enhanced the overall governance capacity of the region. Additionally, eco-environmental construction has also injected the endogenous power of green innovation into the long-term economic development model of the region [228], prompting the transformation of traditional resource-dependent industries into technology-intensive industries, and the formation of a virtuous cycle mechanism of economic development and ecological restoration; at the level of the social structure, sustained environmental improvement has driven the systematic innovation of cross-sectoral cooperation and facilitated the in-depth fusion of social organizations and the network of community governance [229], so as to build up a long-term cooperation system in which the whole society participates in ecological environmental protection together.

However, the process of ecological environment construction may also bring about some negative impacts. Current research analysis of it can also be divided into two aspects: direct impact and indirect impact. Direct negative impacts mainly involve the disruption of traditional industrial structures by regional ecological environment construction [230], which exacerbates local employment pressures and suppresses economic vitality. Some traditional enterprises may face stricter pollution control requirements [231], leading to increased production costs, decreased profitability, and further issues such as unemployment, business relocation, and slowed economic growth. The instability of the employment market also presents challenges for further regional development [232]. Tangshan City, Hebei Province, implemented an ultra-low emission transformation of steel in 2018, shutting down a cumulative total of 47 highly polluting steel mills by 2023 [233], reducing crude steel production capacity by 42 million tons, directly leading to 98,000 people losing their jobs in the steel industry, indirectly affecting the employment of about 150,000 people, and decreasing the city’s secondary industry employment rate by 2.7 percentage points. Indirect negative impacts mainly concern the short-term adverse effects on the agriculture, forestry, animal husbandry, and fishing industries, as well as regional coordinated development. While ecological environment construction has overall enhanced the stability of agricultural ecosystems, strict water resource protection policies may restrict irrigation water for agriculture, thereby affecting crop yields [234]. Large-scale wetland restoration or water conservancy adjustments, if not fully considering the needs for farmland irrigation and fisheries, may also reduce the production efficiency of related industries [235]. The case of Hongze Lake shows that the restoration of large-scale wetlands that ignores the demands of agriculture and fishery may directly lead to labor losses and a reduction in regional output [236]. Additionally, due to differences in development stages, fiscal strength, and industrial foundations [129], some regions may face greater difficulties in adapting, struggling to balance environmental protection with economic development in the short term, thus widening the development gap between regions [237]. Some environmental protection policies, if lacking scientific planning and supporting measures, could exacerbate issues such as uneven resource allocation and irrational industrial migration [238]. In the long run, the construction of the ecological environment may solidify the difficulties in the transformation of some traditional industries, create a crowding-out effect on new industries [239], and inhibit the vitality of the diversified development of the regional economy. At the same time, the inequality of social strata in access to ecological resources and distribution of environmental dividends will be strengthened, resulting in intensification of internal conflicts and tensions within the community [240].

Existing studies have systematically assessed the impacts of regional ecological environment construction from both direct and indirect dimensions. These studies have not only revealed the positive outcomes through systematic analysis but also objectively identified the negative challenges, thereby demonstrating the comprehensiveness and criticality of the research. However, the existing results still have obvious deficiencies: firstly, more attention is paid to the short-term effects of direct impacts, and there is a lack of systematic tracking of long-term dynamics; secondly, the analysis of indirect impacts fails to effectively integrate interdisciplinary perspectives, which leads to insufficient depth in the analysis of the complex mechanisms; and thirdly, there is a lack of in-depth exploration of the interaction of the negative impacts among regions, which restricts the scientific nature of the optimization of the global policies. In the future, in order to enhance the inclusiveness and sustainability of policy design, it is advisable to strengthen long-term tracking research and pay attention to regional synergistic governance mechanisms.

4.3. Analysis of Research Methodology

In exploring the ecological environment assessment at the regional level in China, econometric methods provide powerful tools for quantifying the complex relationships between the ecological environment and its influencing factors. Existing literature categorizes these econometric methods into two major categories: linear methods and nonlinear methods, based on the relationships between research variables. Linear methods mainly include simple linear regression and multiple linear regression. Simple linear regression is used to analyze the linear impact of a single independent variable on a dependent variable [241], exploring the impact of a particular factor on the regional environment, or examining the effect of regional environmental changes on a specific factor. Multiple linear regression, on the other hand, is used to analyze the combined effects of various factors on ecological environment quality, or to study the impact of various regional environmental factors on a specific factor [242], aiming to identify key influencing factors and develop corresponding policy measures. Nonlinear methods mainly include threshold effect regression and spatial econometric analysis methods. Threshold effect regression is used to study whether the impact of an independent variable on a dependent variable changes at a certain threshold, meaning that when the explanatory variable exceeds a certain threshold value, its impact on the dependent variable may undergo a significant change [243]. In the context of regional ecological environment construction, this method is often employed to determine whether pollution concentrations above a certain threshold significantly worsen ecosystem pollution [244], or to explore the differential impact of a factor on regional ecological environments across various value ranges [245]. Due to the spatial spillover effects often present in ecological environment issues [246], traditional econometric methods may not accurately capture this spatial dependency. Spatial econometric analysis addresses this by introducing spatial weight matrices [247] and constructing a Spatial Autoregressive Model (SAR), Spatial Error Models (SEMs), and a Spatial Durbin Model (SDM), among others. These spatial econometric models effectively handle spatial autocorrelation and spatial heterogeneity, quantifying ecological environmental impacts between neighboring regions [248] and revealing regional spatial interaction mechanisms. Related research using spatial econometric models has shown that factors such as industrial pollution emissions [249], agricultural non-point source pollution [250] and urbanization expansion [251] not only affect the local ecological environment but also have repercussions for the ecological environments of other regions.

Linear regression methods have the advantages of simple modeling and intuitive interpretation by quantifying the linear effects of single or multiple factors on the ecology [252], but their assumption of linear independence among variables makes it difficult to capture nonlinear relationships or interaction effects [253], leading to limitations in the interpretation of complex ecosystems [254]. Threshold effect regression is able to identify the critical value of variable impacts and reveal the law of mutation [255], but the threshold setting mostly relies on subjective experience, which may ignore the dynamic mechanism of continuous variable changes [256]. The spatial econometric model effectively solves the spillover effect [257] and spatial dependence of ecological impacts among regions by introducing a spatial weight matrix [258], but the sensitivity to spatial weight is high, and the model is complex and weakly interpreted, which is easily constrained by data quality in practical applications.

In addition to econometric methods, studies exploring ecological environment influencing factors can also utilize various other methods to comprehensively and multidimensionally uncover the relationships between the ecological environment and other factors. Existing studies classify these methods into two categories based on their core analytical objects: methods focusing on multi-factor interaction mechanisms and methods focused on the internal structure and function of ecosystems. Methods based on the former include Structural Equation Modeling (SEM) and System Dynamics Modeling (SDM). SEM is a multivariate statistical analysis method that can simultaneously handle the relationships between multiple dependent and independent variables, quantifying the impact of latent variables on the ecological environment [259]. This method not only analyzes direct effects but also reveals indirect and mediating effects, providing a more comprehensive understanding of the mechanisms behind ecological environment impacts [90]. For example, SEM can be used to analyze the composite impacts of economic development, policy interventions, and social behavior on the ecological environment and quantify the contribution of each factor [260]. SDM, a dynamic modeling approach based on feedback mechanisms, is suitable for analyzing the long-term behavior of complex systems [261]. In ecological environment research, this model can simulate the interactions between various factors and the ecological environment, revealing trends in ecological environment changes under different scenarios [262]. For instance, a comprehensive model containing economic, social, and environmental subsystems can be constructed to quantify the impact of different policy interventions on the ecological environment, providing scientific support for policymaking [263]. Methods focused on the internal structure and function of ecosystems mainly refer to Ecological Network Analysis (ENA). This method analyzes the impacts of human activities on the stability and functions of ecosystems by constructing material flow, energy flow, and information flow networks [264]. In one study, ENA was used to assess the impacts of agricultural activities on water resource systems, to quantify the impacts of agricultural water use on riverine ecosystems, and to make recommendations for optimizing water allocation [265]. In another study, ENA was applied to evaluate the sustainability of Guangdong’s regional economy, focusing on network indicators such as ascendency to assess system resilience and efficiency [266]. While centered on economic systems, the methodology offers insights applicable to ecological resilience and sustainability assessments.

Structural equation modeling can comprehensively analyze multivariate path relationships and reveal the combined effects of direct and indirect influences [267]. However, it requires the support of large sample data. In addition, model construction is prone to subjective assumptions, which may result in biased outcomes [268]. The system dynamics model simulates the long-term behavior and policy scenarios of ecosystems through feedback mechanisms [269]. It is well-suited for deducing dynamic trends; however, its parameter calibration largely depends on empirical judgments, making validation challenging and thereby limiting its broader applicability [270]. Ecological network analysis evaluates ecosystem function and vulnerability based on the network structure of material and energy flows [271]. Although it provides a scientific basis for ecological restoration, data acquisition is complex and the model construction threshold is high [272], making it difficult to promote widely in practical applications.

5. Conclusions

In today’s era of environmental change and resource constraints, the contradiction between rapid economic development and ecological environmental protection is becoming increasingly evident. As the world’s largest developing country, China’s ecological environment quality significantly influences global economics, environment, and climate. Concurrently, with China’s rapid economic growth and urbanization, regional ecological environmental issues have become critical factors restricting the social and economic development of various regions within the country. In the field of regional ecological environment assessments, scholars have proposed a variety of evaluation methods and dimensions based on different theoretical frameworks and practical needs, and have conducted in-depth explorations of the impacts of ecological environment construction. This paper aims to comprehensively present the research findings in this field by dividing the literature into three main sections: methods of regional ecological environment assessment, dimensions of regional ecological environment assessment, and the exploration of the impacts of regional ecological environment construction. Through this structural arrangement, the paper demonstrates the multidimensionality and comprehensiveness of regional ecological environment assessment research, providing a more comprehensive perspective for future studies in this area.

The second part of the paper summarizes the current methods of regional ecological environment assessment in China, where quantitative analysis methods dominate. These include the Single Indicator Method, Indicator System Method, data envelopment analysis (DEA), ecological footprint method from a sustainability perspective, and ecosystem service value assessment method from an ecological service perspective. With the application of remote sensing technology and machine learning, the precision and timeliness of ecological environment assessments have significantly improved. The third part summarizes regional ecological environment assessment studies across different dimensions. This section explores five dimensions—national, provincial, urban and urban clusters, rural and county levels—revealing the differences and complexities among these dimensions. The fourth part delves into the exploration of the effects of regional ecological environment construction, analyzing the factors and effects influencing ecological environment construction in China. In the analysis of the factors affecting regional ecological environment, both tangible and intangible factors are considered. In the analysis of the effects of regional ecological environment construction, the paper separately analyzes positive and negative effects. Finally, the fourth part also discusses the main tools and methods used to measure these impacts.

In summary, current research on regional ecological environment assessments in China involves evaluation methods and analytical dimensions, as well as the analysis of influencing factors and effects. However, there are still areas with room for improvement in each of these aspects. Regarding the selection of assessment methods, each method has its limitations in terms of applicability. Therefore, when conducting ecological environment research across different dimensions, it is important to choose appropriate assessment methods based on the characteristics of the research objects and content. These methods play a crucial role in revealing the complex relationship between ecological environments and socio-economic activities. Regarding influencing factors, studies show that various factors can positively contribute to the improvement of ecological environment quality. However, in practice, there are also many factors that restrict the effectiveness of ecological environment construction. In terms of effect analysis, ecological environment construction has a significant positive impact on social welfare and economic vitality, but it also brings short-term negative effects in traditional industries and agricultural production. This indicates that balancing economic development and environmental protection goals remains a key issue in promoting environmental protection.

Through three sections—methods of regional ecological environment assessment, analytical dimensions, and the exploration of influencing factors and effects—it is evident that regional ecological environment assessment in China should further strengthen the interconnectivity across different levels and dimensions, especially with regard to the construction of evaluation systems that need to consider regional differences and the implementation of policies. Based on the review of existing research, this paper identifies the following promising development directions for future research. In terms of regional ecological environment assessment methods, future research would develop in two directions. On the one hand, with the use of machine learning methods, future research could break through the constraints of data availability gradually through algorithm iteration, and achieve the assessment of ecological environment quality in specific regions. On the other hand, with the accumulation of big data, the assessment of regional ecological environment could incorporate more indicators into consideration, improving the accuracy of the assessment. In terms of the assessment scale of regional ecological environment, with the refinement of regional governance in China, future research would gradually transition from macro to micro, focusing on the investigation of communities within cities and rural areas outside cities. In terms of the influencing factors of regional ecological environment, with the improvement of policy evaluation models including Difference-in-Differences, future research would be enriched in the field of evaluating policy effectiveness in the short term, and new discoveries would be made in the long term with the development of econometric models and the integration of multiple disciplines.