Evaluation of Provincial Carbon-neutral Capacities in the Yellow River Basin Using DPSIR

Evaluation of Provincial Carbon-neutral Capacities in the Yellow River Basin Using DPSIR | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluation of Provincial Carbon-neutral Capacities in the Yellow River Basin Using DPSIR Jian Xu, Haiying Wang, Keyu Zhao, Zhi Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1838219/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract The Yellow River basin plays an important role in China's economic and social development and ecological security. To study the changes in the trend and driving mechanisms of the carbon-neutral capacity of the Yellow River basin and provide a theoretical reference value for a comprehensive realization of carbon neutrality in China in 2060, the corresponding subsystems based on the driving-force-pressure-state-impact-response (DPSIR) model framework were established. Furthermore, a DPSIR index system, which consisted of 39 factors reflecting the carbon-neutrality capacity and ecological environment state of the Yellow River basin, was proposed. The DPSIR subsystem layers’ weights were determined using an expert evaluation method. The global entropy method was used to obtain the weights of the 39 indicators, the evaluation model of carbon-neutral capacity was proposed to calculate the comprehensive evaluation value of the provincial carbon-neutral capacities comprehensive evaluation Index (CCCEI) in the Yellow River basin. Our results indicate that, from the perspective of the DPSIR subsystems, the evaluation value of the carbon-neutral capacity driving subsystem in the Yellow River basin exhibited an overall upward trend from 2008 to 2019. However, the evaluation value of the carbon-neutral capacity pressure subsystem decreased slightly in some years, while the overall trend increased marginally. The carbon-neutral capacity status subsystem evaluation value was at a lower level and requires further improvement. The evaluation value of the carbon-neutral capacity impact subsystem had a certain fluctuation, and the evaluation value of the carbon-neutral capacity response subsystem improved rapidly and steadily afterward. The final results indicated that, from 2008 to 2019, the carbon-neutral capacities of the provinces in the Yellow River basin were in a state of rapid development and had achieved a grade leap. However, seven provinces had carbon-neutral capacity levels at Grade III standard in 2019, thereby leaving scope for substantial improvement. DPSIR model carbon-neutral capacities comprehensive evaluation index (CCCEI) carbon-neutral Yellow River basin evaluation index system Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction Unchecked emissions of greenhouse gases lead to global warming and aggravate problems such as ecological and environmental deterioration and energy crisis. Therefore, more and more countries have switched to developing low-carbon economies as an effective way for sustainable development since the advent of the 21st century. As a special low-carbon development mode, carbon neutrality promotes balancing of carbon emissions and carbon absorption through the innovation and reform of technologies and systems, industrial transformation and upgrading, and promotion and utilization of clean energy. Finally, the coordinated development of the society, economy, environment, and energy is being emphasized. The terrain of the Yellow River basin is high in the west and low in the east. The western region of the basin is covered with snow throughout the year. Its central area is covered with loess; therefore, the soil and water losses are significant in this region. The eastern region of the basin is comprised mainly of the alluvial plain of the Yellow River. The Yellow River basin spans nine provinces and autonomous regions and plays an important role in China's economic and social development and ecological security. Therefore, the objective and comprehensive evaluation of the carbon-neutral development level of the Yellow River basin is of high significance for the sustainable development of the provinces and cities in the Yellow River basin. There is hardly any literature on the development level of carbon neutrality. However, the concept and method of the sustainability evaluation system, evaluation of regional low-carbon economy, ecological level, and analysis of carbon emissions are relatively mature. The selection of evaluation indicators is broadly divided into three categories: basic, core, and tendency indicators (which vary based on research objectives). The selection of indicators depends on the individual scholar Li et al. (2020) in their research on low-carbon economy and the development of agricultural modernization, based on the connotation definition of agricultural modernization, introduced an appropriate agricultural low-carbon evaluation index based on the evaluation index system of agricultural modernization in Heilongjiang Province. This index provides a reference for the construction and development of agricultural modernization in Heilongjiang Province from the perspective of a low-carbon economy. Xiang et al. (2019) combined low-carbon indicators and distribution network operation, starting from the low-carbon contribution of the new distribution network technology and combining with the low-carbon elements of each part of the distribution network. The evaluation index for the low-carbon operation of the distribution network is constructed from four evaluation aspects: low-carbon power, low-damage network, load shifting, and terminal to reduce emissions. The analytic hierarchy process (AHP) and anti-entropy method were used to determine the index combination weights by combining subjective and objective empowerment. A fuzzy comprehensive evaluation method of low-carbon operation by constructing a subordinate function and fuzzy comprehensive evaluation model was established. Dong et al. (2021) focused on the characteristics of the transportation industry to study the low-carbon traffic index system. A balanced scorecard card model was used to establish a low-carbon transportation evaluation index system for the Beijing-Tianjin-Hebei region. Next, the key performance indicators were selected to establish the evaluation index system for low-carbon transportation construction in the Beijing-Tianjin-Hebei region. Shao et al. (2010) studied the carbon emission problem in urban construction and development and considered the actual situation of the development of a low-carbon economy in China. A driving-pressure-state-impact-response (DPSIR) model framework was adopted to establish an indicator system for low-carbon urban construction and evaluation. Li et al. (2014) constructed a low-carbon economy evaluation index system for Hebei Province based on the DPSIR framework model. The relevant statistical data obtained using the AHP were analyzed to evaluate and analyze the low-carbon economic development status of Hebei Province from 2005 to 2010. Lu et al.(2019) established a DPSIR-model-based comprehensive evaluation system for urban river sustainable development. By monitoring the ecological indicators during the restoration process of the Nanfei River, the key factors affecting the ecology are identified to provide a complete and reliable perspective for the ecological impact of river restoration. In addition, useful guidance for improving the state of urban and non-urban river ecosystems is provided. Liu et al.(2018) constructed a DPSIR framework for evaluating the sustainability of marine industrial parks and applied it to evaluate the sustainability of the Shishi Marine Biotechnology Park in Fujian Province of China during 2013–2016. The AHP and entropy method were used to calculate the weights of indicators. The index variable and the evaluation model were transformed uniformly to obtain the normalized sustainability score. Chen et al.(2020) collected 21 indicators—spanning the economic, social, and environmental sustainability levels—to develop an indicator system. Furthermore, they proposed an integrated TOPSIS-ORM approach to evaluate the sustainability level of shrinking cities in the northeast based on the interactions among various indicators. Zhao et al.(2021) constructed an evaluation index for 35 major cities in China. They evaluated the sustainability of the urban ecological resilience network using the ecological network analysis method. Delgado et al.(2021) used the DPSIR models to analyze the ecological status of the Chilean coastal region and its main socio-ecological effects. The results of the family surveys and interviews with local experts and social activists were verified. Malekmohammadi et al.(2017) discussed wetland vulnerability from the perspective of using the DPSIR framework to analyze human and environmental systems. The vulnerability of wetland ecosystem services was also assessed. Threat indicators were characterized based on their significance, severity, and occurrence probability. Guo et al.(2018) established an evaluation index system for Urban low-carbon competitiveness from the perspective of the driving force and resistance. They performed principal component and cluster analyses to evaluate and classify Urban low-carbon competitiveness in the Wuhan metropolitan area. They used a fuzzy analytic hierarchy process to test the results. Furthermore, an objective index analysis was used to identify the obstacles encountered in each city. Finally, the development of Urban low-carbon competitiveness was simulated through system dynamics modeling to determine the problems that exist in the city. Fang et al.(2021) used the DPSIR framework to study the Hainan Island in China. They used the grey correlation analysis and ideal solution similarity sorting technology to perform coupling coordination analysis and study the sustainable development index of the blue economy of the Hainan Island. Duan et al.(2016) used the AHP and entropy weight method to construct an evaluation system for the low-carbon economic development level in Dalian. They used this system to frame a comprehensive evaluation index of the low-carbon economic development level of Dalian from 2005 to 2014. Ma et al.(2017) proposed a new evaluation method for urban green traffic planning. This method was based on the central point triangle whitening weight function and entropy weight analytic hierarchy process. It combined the advantages of the grey evaluation and entropy weight methods and used the grey evaluation method as its central model. The weight of each index was determined using the entropy and AHP composite model. The whitening and comprehensive clustering coefficients of each index were calculated using the central point triangle whitening weight function, which overcomes the subjectivity of the traditional method in determining the weight vector and improves the scientific basis for the evaluation by combining qualitative and quantitative system analyses. Fabianek et al.(2020) adapted the AHP based on economic and ecological standards to perform multi-standard decision analysis and proposed an evaluation framework for green power supply for plug-in electric vehicles in Germany. Cheng et al.(2018) selected and analyzed 21 indicators using correlation analysis, fuzzy rough set, and entropy weight method, based on the connotation of regional green competitiveness. They framed a regional green competitiveness index. The aforementioned studies discuss the evaluation indicators and evaluation methods for low-carbon and related industries or economies in different regions or provinces. In these studies, region- and industry-specific parameters are factored in while framing the index. Furthermore, the situation corresponding to an index is relatively simple. However, such specific and relatively simple indexes and evaluations methods are not suitable for evaluating multi-provinces river basins and heterogeneous regions because of their many and diverse characteristics. Li et al.(2012) studied the ecological safety index system of the basin and proposed an improved DPSIR model by including five types of indicators of the basin ecological safety indicators and covering the overall situation of ecological safety disaster change. They also verified the applicability and importance of the improved DPSIR model in basin-scale ecological safety evaluation. Niu et al.(2021) built an evaluation index, which included six levels and 20 indicators for the carbon neutrality ability evaluation problem, as also the factors affecting it, across provinces. They also proposed an improved TOPSIS method using subjective and objective combinations for analysis and calculation. They report that the use of renewable energy, maintaining ecological environmental quality, and low-carbon technology significantly affecting the carbon-neutrality capacity. Finally, some suggestions are proposed to accelerate carbon neutrality. Liu et al.(2021) empirically analyzed the impact of the three subsystems of ecological civilization on carbon emission intensity using the Chinese provincial panel data for years 2004–2016 and the Dubin space model based on the Stepat model. Cao et al.(2021) proposed an evaluation model based on an improved object element topology model that combined the DPSIR model, entropy weight method, cloud model, and cloud entropy optimization algorithm. Considering Jiangsu Province as an example, the ecological environment performance of China in 2019 was evaluated using this model. Wei et al.(2019) used pressure-state-response (PSR) methods to identify the factors that affect carbon emissions in China. Empirical studies based on provincial panel data and structural equation models suggest a strong relationship between carbon emissions and the factors that affect them. Jiang et al.(2021) studied the impact of internal and external investment structural changes on global carbon emissions. They used a structural decomposition analysis to decompose global carbon emission changes into six factors, namely, carbon emission intensity, domestic input structure, international investment structure, consumption pattern, consumption, and population. Duan et al.(2021) established DPSIR model by Tapio decoupling analysis method, and the effects of 30 quantitative indexes on water environmental health in Chaohu Lake basin were systematically evaluated. Chen et al.(2019) quantifies final demand by looking at China's provincial CO 2 emissions from different angles. Quantitative study of carbon dioxide emissions by provincial production activities, consumption and income in China. Comparative study of more than 30 provinces in China from different perspectives. The results show that production emits more carbon dioxide than consumption and income in highly industrialized provinces. In the developed eastern coastal provinces and resource-rich provinces, the policy focuses on the demand side and the supply side. To sum up, most of the research objects of low-carbon development have been urban areas or specific industries. However, research on the evaluation of low-carbon development levels in river basins and multi-provincial or heterogeneous regions is rare. In addition, most of the research focuses on the current low-carbon economy. Considering the vision and goal of carbon neutrality, few studies have integrated the concept of carbon neutrality into research to evaluate the carbon-neutral development level. Most of the current evaluation indicators for the low-carbon development level are common for carbon emissions and carbon absorption. However, no evaluation indicators have been established specifically for carbon transmission. Research suggests that the DPSIR model can represent the concept and structure of a composite system effectively, and it can be used to evaluate low-carbon or carbon-neutral development levels in many river basins and/or across provinces. Therefore, this study extends the present research to develop the DPSIR analysis model for the evaluation of carbon-neutrality capability. An ecological evaluation index is established based on the actual situation in respect of carbon emissions, carbon absorption, economy, population, technology, resources, environment, etc. of the Yellow River basin. The driving mechanism, pressure, state, impact, and response analyses of the provincial carbon-neutrality evaluation system in the Yellow River basin and other indicators were improved. Combined with the data of social and economic statistics, the AHP and entropy weight methods were used to evaluate the carbon-neutrality performance of nine provinces in the Yellow River basin. The collected data were used to verify the applicability and significance of the improved DPSIR model for the evaluation of carbon-neutral capacity in the evaluation of the carbon-neutral development level of the Yellow River basin to provide a theoretical reference for its sustainable development. 2. Construction Of Provincial Carbon-neutral Capacity Evaluation Index System For The Yellow River Basin 2.1 DPSIR model The driving-pressure-state-impact-response (DPSIR) model has evolved from the pressure-state-response (PSR) model proposed by the Organization for Economic Cooperation and Development and the driving-pressure-response (DPR) model proposed by the United Nations Commission on Sustainable Development. It is comprehensive, systematic, holistic, and flexible. The model can reveal the causal relationship between the environment and economy. The DPSIR model divides the system into five factors: driving, pressure, status, impact, and response. Figure 1 depicts the DPSIR concept model for the Yellow River basin and reflects the interaction process of carbon-neutral influencing factors. Driving (D) represents the driving factors of carbon-neutral capacity caused by provincial economic development in the Yellow River Basin and pressure (P) refers to the pressure of the economic development activities on the carbon-neutral capacity of the basin. State (S) is the state level of the provincial carbon-neutral capacity under pressure. Influence (I) is the feedback result and impact of various state levels on the provincial carbon- neutral capacity in the Yellow River Basin. Response (R) refers to various positive measures and countermeasures adopted to enhance the carbon-neutral capacity of provinces in the basin. 2.2 Construction of carbon-neutral-capacity evaluation index system To objectively and scientifically evaluate the carbon-neutral capacity of the Yellow River basin, this study follows the principles of the DPSIR model: science, system, compatibility, regional, hierarchy, operability, and regional economic and social development. The evaluation index system includes three levels: the target layer, subsystem layer, and index layer. The determined index layer comprises 37 specific indicators. It determines its attributes as positive (+) or negative indices (-) for the index characteristics. The specific carbon-neutral-capacity evaluation index system is listed in Table 1 . Table 1 Carbon-neutral-capacity evaluation index system for the Yellow River basin Target layer Subsystem layer Index layer Variable Description and description Properties Notes Evaluation of provincial carbon neutral capacity in the Yellow River Basin Driving (D) GDP per capita d 1 Measure people's living standard (Yuan) + Niu et al.2021. Zhang et al.2019. Shao et al.2010. Li et al.2014. Lu et al.2019. GDP per capita growth rate d 2 Measure the growth rate of regional economic growth (%) + Niu et al.2021. Provincial Population growth rate d 3 Measure the regional population growth rate (%) - Niu et al.2021. Zhang et al.2019. Shao et al.2010. Lu et al.2019. Provincial Permanent population d 4 Characterizing the provincial population distribution (10,000 people) - Niu et al.,2021. Urbanization level d 5 The process and degree of population aggregation toward the city (%) + Fang et al.2021. Niu et al.2021. Shao et al.2010. Li et al.2014. Lu et al.2019. Industrial output value growth rate d 6 Industrial production growth degree (%) - Fang et al.2021. Niu et al.2021. Li et al.2016. Approved patents ratio d 7 Regional scientific and technological innovation capacity + Fang et al.2021. Li et al.2014. Afforestation areas d 8 Measure the ecological improvement capacity of the area (ha/person) + Fang et al.2021. Lu et al.2019. Pressure (P) Energy consumption per capita p 1 Capacity to consume energy per person (tons of standard coal/person) - Fang et al.2021 Niu et al.2021. Jiang et al. 2021 . Carbon emissions per capita p 2 Total carbon emissions per person (ton/person) - Cao et al.2021. Wei et al.2019. Jiang et al. 2021 . Cars per capita p 3 Measure traffic carbon pollution (vehicle/person) - Fang et al.2021 Niu et al.2021. Average annual heating days p 4 Impact of heating (days) - Wei et al.2019. living area per capita p 5 Impact of construction industry (m 2 /person) + Fang et al.2021 Niu et al.2021. Wei et al.2019. Jiang et al. 2021 . Status (S) proportion of renewable energy power s 1 Measure the regional dependence on traditional energy generation (%) + Fang et al.2021 Niu et al.,2021. Carbon absorption to carbon emission ratio s 2 Total regional carbon absorption/total carbon emissions (%) + Wei et al.2019. Jiang et al. 2021 . GDP proportion of the secondary industry s 3 GDP of processing and manufacturing industry/GDP of region (%) - Fang et al.2021 Niu et al.2021. Cao et al.2021. Jiang et al. 2021 . Proportion of national hygiene cities s 4 The province was selected as National Health City/all cities in the province (%) + Zhang et al.2019. GDP proportion of the tertiary industry s 5 Service sector GDP gross value/regional GDP gross value (%) + Fang et al.2021 Niu et al.,2021. Cao et al.2021. Jiang et al. 2021 . Cultivated land area per capita s 6 Per capita cultivated land area (mu / person) + Niu et al.2021. Regional air qualified rate s 7 Number of days the air quality meets the standard/365 + Niu et al.2021. Cao et al.2021. Jiang et al. 2021 . Annual average flow s 8 liquid volume flowing through a section (m 3 / s) + Zhang et al.2019. Water qualified rate s 9 Number of days of Yellow River / (%) + Zhang et al.2019. Impact (I) Annual average temperature change rate i 1 Measure the stability of the regional temperature (℃) - Wei et al.2019. Carbon dioxide concentration i 2 Regional air CO 2 contains (%) - Niu et al.2021. Wei et al.2019. Air pollution index i 3 Measures the quality of regional air quality - Niu et al.2021. Wei et al.2019. Jiang et al. 2021 . Proportion of soil erosion i 4 Provincial soil and soil loss area/provincial total land area (%) - Zhang et al.2019. Comprehensive pollution index of water quality i 5 The method of evaluating the water environmental quality is divided into six levels - Niu et al.2021. Zhang et al.2019. Waste water discharge volume i 6 Total annual wastewater discharge (ton/year) - Fang et al.2021. Niu et al.2021. Response (R) Proportion of photovoltaic power r 1 Solar Power Generation / Total Power Generation: (%) + Fang et al.2021. Percentage of the forestry and grass coverage r 2 Measure the ability of the natural environment to absorb carbon absorption (%) + Fang et al.2021 Niu et al.2021. Zhang et al.,2019. Wei et al.2019. Public transport travel ratio r 3 Environmental impact of carbon emissions from traffic (%) + Fang et al.2021 Wei et al.2019. Proportion of wind power r 4 Wind power generation/Total power generation: (%) + Fang et al.2021 Comprehensive utilization percentage of industrial solid waste r 5 Comprehensive utilization of industrial solid waste accounts for industrial solid waste production(%) + Fang et al.2021 Niu et al.2021. Wei et al.2019. Jiang et al.2021. Centralized treatment rate of urban sewage r 6 Percentage of sewage treated by urban centralized sewage treatment plant and urban sewage discharge (%) + Wei et al.2019. Jiang et al.2021. Low-carbon economic development plan r 7 Government response to carbon neutrality + Niu et al.2021. Wei et al.2019. Urban road areas per capita r 8 Reacting the congestion of urban traffic (m 2 / person) + Wei et al.2019. Hydropower station capacity r 9 Measure the dependence on new energy generation (GW) + Zhang et al.2019. 3. Evaluation Of Provincial Carbon-neutral Capacity Level In The Yellow River Basin 3.1 Data source and collection methodology The data on economic and social development were mainly sourced from China Statistical Yearbooks from 2008 to 2019 and included data for nine Yellow River basin provinces. The data on the use of resources were sourced mainly from the China Energy Statistical Yearbooks from 2008 to 2019. The data on environmental quality and environmental governance were sourced mainly from the 2008–2019 China Environmental Statistics Yearbooks, China Bulletin on the Status of the Marine Environment and the Department of Ecology and Environment of the nine Yellow River Basin provinces. The indicator data on per capita carbon emission, annual average temperature change rate, GDP proportion of secondary industry, tertiary industry GDP proportion, and provincial health cities were calculating using their respective formulas from the index description and explanation given in Table 1 . 3.2 Evaluation-index weight determination based on global entropy value method After the evaluation index system was established, an appropriate evaluation method was selected to evaluate the development level of the system. Commonly used comprehensive evaluation methods include hierarchical analysis, fuzzy comprehensive evaluation, main component analysis, factor analysis, and entropy methods. The entropy method is an objective empowerment method that calculates the degree of numerical dispersion among the indicators. However, the traditional entropy method can handle only cross-sectional data. It cannot handle the panel data of multi-index system spanning years. Therefore, this study used the global entropy method to determine the weight of the evaluation index system for the provincial carbon-neutral capacity of the Yellow River basin. (1) Standardized processing of index data Because the original data of the N index may have different units and dimensions, these data must be standardized to obtain standardized data \({\text{z}}_{{\alpha }\text{i}\text{j}}\) : $${\text{z}}_{{\alpha }\text{i}\text{j}} = \left\{\begin{array}{c}\frac{ {\text{x}}_{{\alpha }\text{i}\text{j}}-{\text{x}}_{\text{m}\text{i}\text{n}}}{{\text{x}}_{\text{m}\text{a}\text{x}}-{\text{x}}_{\text{m}\text{i}\text{n}}} forward indicators\\ \frac{ {\text{x}}_{\text{m}\text{a}\text{x}}-{\text{x}}_{{\alpha }\text{i}\text{j}}}{{\text{x}}_{\text{m}\text{a}\text{x}}-{\text{x}}_{\text{m}\text{i}\text{n}}} Negative indicators\end{array}\right.$$ 1 where \({{\text{x}}_{\text{m}\text{i}\text{n}} \text{a}\text{n}\text{d} \text{x}}_{\text{m}\text{a}\text{x}}\) represent the minimum and maximum of an index in all years and in all provinces, respectively; \({{\text{x}}_{{\alpha }\text{i}\text{j}} \text{a}\text{n}\text{d} \text{z}}_{{\alpha }\text{i}\text{j}}\) , respectively, represent the values of item α of the j th province before and after standardization in year i. (2) Normalization of index standardization data: $${\text{y}}_{{\alpha }\text{i}\text{j}}=\frac{{\text{z}}_{{\alpha }\text{i}\text{j}}}{\sum _{\text{i}=1}^{\text{m}}\sum _{\text{j}=1}^{\text{n}}{\text{z}}_{{\alpha }\text{i}\text{j}}}$$ 2 (3) Calculate the information entropy of each index \({\text{E}}_{{\alpha }}\) : $${\text{E}}_{{\alpha }}=-\text{k}\sum _{\text{i}=1}^{\text{m}}\sum _{\text{j}=1}^{\text{n}}{\text{y}}_{{\alpha }\text{i}\text{j}}\text{l}\text{n}{\text{y}}_{{\alpha }\text{i}\text{j}}$$ 3 where \(\text{k}=1/\text{l}\text{n}(\text{m}\times \text{n}\) \()\) . When \({\text{y}}_{{\alpha }\text{i}\text{j}}=0\) , set \({\text{y}}_{{\alpha }\text{i}\text{j}}\text{l}\text{n}{\text{y}}_{{\alpha }\text{i}\text{j}}\) =0. Calculate the redundancy of each index \({\text{D}}_{{\alpha }}\) : $${\text{D}}_{{\alpha }}=1-{\text{E}}_{{\alpha }}$$ 4 Calculate the weights of each index \({\text{w}}_{{\alpha }}\) : $${\text{w}}_{{\alpha }}=\frac{{\text{D}}_{{\alpha }}}{\sum _{{\alpha }=1}^{\text{N}}{\text{D}}_{{\alpha }}}$$ 5 3.3 DPSIR model subsystem level evaluation The DPSIR model subsystem layer evaluation formula is as follow: $${\text{S}}_{\text{d}\text{p}\text{s}\text{i}\text{r}}=\sum _{{\alpha }=1}^{\text{N}}{\text{w}}_{{\alpha }}\times {\text{y}}_{{\alpha }\text{i}\text{j}}$$ 6 where \({\text{S}}_{\text{d}\text{p}\text{s}\text{i}\text{r}}\) is the subsystem layer evaluation value of the DPSIR model. The five subsystem evaluation values are as follows: driving subsystem evaluation value (S d ), pressure subsystem evaluation value (S p .), state subsystem evaluation value (S s ), impact subsystem evaluation value (S i .), and response subsystem evaluation value (S r ). 3.4 Comprehensive evaluation index and capacity level of provincial carbon-neutral capacity of the Yellow River basin The weight (W dpsir ) of the DPSIR subsystem layer was determined using the expert evaluation method. The five subsystem weights were as follows: driving (w d ), pressure (W p .), state (W s ), impact (W i .) and response (W r ) weights. The carbon-neutral capacities comprehensive evaluation Index (CCCEI) was obtained as $$\text{C}\text{C}\text{C}\text{E}\text{I}={\text{S}}_{\text{d}}\times +{\text{S}}_{\text{p} }\times {\text{W}}_{\text{p}}+{\text{S}}_{\text{s}}\times {\text{W}}_{\text{s}}+{\text{S}}_{\text{i}}\times {\text{W}}_{\text{i}}+{\text{S}}_{\text{r}}\times {\text{W}}_{\text{r}}$$ 7 Considering the score of the comprehensive evaluation index of the provincial carbon-neutral capacity of the Yellow River basin and referring to the relevant comprehensive index classification method at home and abroad and the actual situation of this study, the grading standards for establishing provincial carbon-neutral、 capacities in the Yellow River basin are listed in Table 2 . Table 2 Classification of carbon-neutral capacities in the Yellow River basin CCCEI Grade Carbon-neutral capacity level ≤ 0.4 Ⅰ Poor (0.4, 0.8] Ⅱ Fair (0.8, 1.2] Ⅲ Average (1.2, 1.6] Ⅳ Good > 1.6 Ⅴ Excellent 4. Empirical Analysis 4.1 Subsystem layer and index layer weights calculation The DPSIR subsystem layers’ weights were calculated by five experts by using expert evaluation method. Next, the global entropy method was used to process 37 index datasets from nine provinces located in the Yellow River basin. The calculation results are listed in Table 3. Table 3. Weights of the provincial carbon-neutral capacity evaluation index system in the Yellow River basin Target layer Subsystem layer Index layer Variable Weight Evaluation of provincial carbon neutral capacity in the Yellow River Basin Driving( D) 0.2021 GDP per capita d 1 0.1069 GDP per capita growth rate d 2 0.1339 Provincial Population growth rate d 3 0.0119 Provincial Permanent population d 4 0.1343 Urbanization level d 5 0.0637 Industrial output value growth rate d 6 0.1442 Approved patents ratio d 7 0.1085 Afforestation area d 8 0.2966 Pressure(P) 0.2071 Energy consumption per capita p 1 0.1912 Carbon emissions per capita p 2 0.2639 Car ownership per capita p 3 0.1186 Average annual heating days p 4 0.1374 living area per capita p 5 0.2889 Status (S) 0.2093 Proportion of renewable energy power s 1 0.2091 Carbon absorption to carbon emission ratio s 2 0.2142 GDP proportion of the secondary industry s 3 0.1357 Proportion of national hygiene cities s 4 0.1275 GDP proportion of the tertiary industry s 5 0.0515 Cultivated land area per capita s 6 0.1014 Regional air qualified rate s 7 0.0480 Annual average flow s 8 0.0603 Water qualified rate s 9 0.0523 Impact (I) 0.1654 Annual average temperature change rate i 1 0.1644 CO 2 concentration i 2 0.2187 Air pollution index i 3 0.2730 Proportion of soil erosion i 4 0.1387 Comprehensive pollution index of water quality i 5 0.0863 Waste water discharge volume i 6 0.1190 Response (R) 0.2161 Proportion of photovoltaic power r 1 0.1399 Percentage of the forestry and grass coverage r 2 0.1942 Public transport mobility and travel ratio r 3 0.0414 Proportion of wind power r 4 0.2067 Comprehensive utilization percentage of industrial solid waste r 5 0.0269 Centralized treatment rate of urban sewage r 6 0.0156 Low-carbon economic development plan r 7 0.1682 Urban road areas per capita r 8 0.0352 Power generation capacity of the Hydropower station r 9 0.1719 4.2 Analysis of factors affecting provincial carbon-neutral capacity development levels in the Yellow River basin The DPSIR subsystem was thoroughly analyzed by comparing the evaluation values of the carbon-neutral capacity DPSIR subsystem of the nine provinces in the Yellow River basin from 2008 to 2019 and combining the characteristics of resources and environment and the specific characteristics of the basin. (1) Driving subsystem The evaluation values of the carbon-neutral capacity driving subsystems in nine provinces in the Yellow River basin generally exhibited an upward trend, as depicted in Fig. 2 . Among the provinces, Inner Mongolia has an extensive and large forest coverage area; therefore, its carbon-neutral driving subsystem evaluation value is at a relatively high level. Sichuan, Shaanxi, and Shandong have the largest GDP per capita growth rate, and relative scientific research patent technological achievements have gradually increased. Economic and technological developments provide sufficient support for carbon neutrality. The evaluation value of the carbon-neutral driving subsystem increased steadily, with a small annual growth in other provinces. The growth rate of various carbon-neutral driving indices slowed. Overall, the carbon-neutral capacity driving subsystem in nine provinces in the Yellow River basin has been maintained at a relatively high level and has achieved good driving results. (2) Pressure subsystem Although the evaluation values of carbon-neutral pressure subsystems in the nine provinces of the Yellow River basin decreased slightly in some years during 2008–2019, the overall trend exhibited a small increase, as depicted in Fig. 3 Carbon emissions per capita increased slightly. However, with economic development and technological progress, the energy consumed per unit economic growth was significantly reduced. Overall, the pressure subsystem evaluation values exhibited an increasing trend. Because the Yellow River basin provinces are located in the north, the heating requirements and people's living conditions put an increased pressure on the resources and environment. This leads to the instability of the pressure subsystem evaluation values and inhibits the growth of the pressure subsystem evaluation values. Although the provinces have actively strengthened the idea of a low-carbon economy in recent years and gradually increased their investment in realizing the carbon-neutral development goal, the pressure problems facing carbon neutrality are difficult to improve significantly on a short timeframe. (3) State subsystem Figure 4 exhibits the state subsystem evolution trend of the provincial carbon-neutral capacity in the Yellow River basin during 2008–2019. Under the dual action of the driving and pressure, the evaluation value of the carbon-neutral state subsystem in Henan, Shandong, and Shanxi was at a low level. In the face of the environmental problems brought about by the provincial economic development in the Yellow River basin, the effectiveness of the measures adopted is not obvious, thereby resulting in pressure on environmental resources. Existing governance measures cannot effectively improve the state of the resources and environment. The rationality of an industrial structure determines its level of economic quality. Industrial transformation and upgrading are conducive to improving the quality of economy. When compared with the tertiary industry, the secondary industry in its growth stage has the characteristics of a high cost of unit economic growth. Inner Mongolia, Qinghai, and Gansu have relatively small proportions of secondary industry and large proportions of renewable energy in power generation. Therefore, these provinces maintain a good carbon-neutral state. Recently, Sichuan, Ningxia, and Shaanxi have actively developed emerging industries, strengthened ecological and environmental protection in the Yellow River basin, and formulated and implemented low-carbon development plans. These measures have effectively eliminated some of the negative impacts of development activities. Presently, the carbon-neutrality states in most provinces in the Yellow River basin are alarming, and the resource and environmental problems are evident. To improve their carbon-neutral states, the resources and environment of the Yellow River basin must be further and rationally utilized, treated, and protected. In general, China's carbon-neutral state subsystem in the Yellow River basin requires to be further improved. (4) Impact subsystem Figure 5 depicts the impact subsystem evolution trends of the provincial carbon-neutral capacities in the Yellow River basin from 2008 to 2019. From the perspective of environmental and resource impacts of provinces in the Yellow River basin, certain fluctuations are evident in the evaluation values of the carbon-neutral impact subsystems of provinces. Although the rates of increase in the temperature in all provinces are negative, the temperatures in all provinces in 2019 exhibited a significant increase compared with those of 2008. The Yellow River basin suffers from serious soil and water losses. Therefore, it has negatively impacted the trend of the carbon-neutral impact subsystem evaluation values. However, the characteristics of low carbon dioxide emissions and a low air comprehensive pollution index in Qinghai and Inner Mongolia ensure that the carbon-neutral impact subsystem in this region remains at a high level. Shaanxi, Henan, Sichuan, Gansu, Shanxi, Ningxia, and Shandong were affected negatively by the provincial economic development. Owing to the excessive development of resources and emissions and wastewater in the process of economic development in each region, the evaluation value of the impact subsystem has decreased to a certain extent, thereby hindering any improvement of the evaluation value of the impact subsystem. Therefore, to mitigate the negative impact of the provincial economic development in the Yellow River basin, it is necessary to establish a reasonable resource and environment utilization mechanism and strictly control the emissions of industrial pollutants. (5) Response subsystem Figure 6 depicts the response subsystem evolution trend of the provincial carbon-neutral capacity in the Yellow River basin from 2008 to 2019. Carbon-neutral driving indices and pressure indices varied across provinces, and the response degree of the measures adopted varied. However, the evaluation values of the response subsystem rapidly and steadily improved after wavy increases and decreases in the early stage. In Henan, Shaanxi, Shanxi, Shandong, and Sichuan, the government organized afforestation drives and invested efforts in scientific and technological research and development to improve the utilization rate of the industrial waste and urban sewage and achieved good results. Ningxia and Gansu promoted public transport to implement a low-carbon economic development plan and promote the improvement of the evaluation value of the provincial carbon-neutral response subsystem in the Yellow River basin. 4.3 Comprehensive evaluation and analysis of provincial carbon-neutral capacities in the Yellow River basin The provincial CCCEI in the Yellow River basin from 2008 to 2019 is depicted in Fig. 7 . A radar map of provincial CCCEI is presented in Fig. 8 . The following features can be observed. (1) Overall, the provincial carbon-neutral capacities in the Yellow River basin were gradually enhanced. During 2008–2019, the provincial carbon-neutral capacities in the Yellow River basin were continuously enhanced, and the CCCEI exhibited an upward trend. Since 2013, the concepts of low-carbon economy and sustainable development have attracted wide attention from the society and governments at all levels. In 2013, the State Council on Printing and Distributing the National Sustainable Development Plan for Resource-based Cities (2013–2020) was issued. The development plan for economic sustainable development in the next seven years was introduced. From 2013 to 2019, nine provinces responded positively to state calls. Governments at all levels need to rationally develop and use environmental resources and strengthen environmental governance and protection in accordance with relevant work arrangements. The strong development of the response subsystem and gradual comprehensive and stable implementation of the regulation measures improved the quality of economic development, the pressure and negative impact on the ecological environment of the Yellow River basin decreased continuously, and the CCCEI increased rapidly. However, driven by the ever increasing population and economic aggregate, the increases in the coal-dominated industrial energy consumption structure and cars per capita ensure that the evaluation values of the pressure and impact subsystems keeps fluctuating, thereby impeding the development of provincial CCCEI in the Yellow River basin. (2) The development of carbon-neutral capacity in the Yellow River basin varies. Qinghai and Inner Mongolia have relatively good ecological environments, large vegetation coverage areas, and underdeveloped economies. Therefore, the provincial CCCEIs were above 0.8 in 2008. From 2008 to 2012, the provincial CCCEI s of Henan, Shanxi, Ningxia, and Shandong were less than 0.8. The overall levels of the carbon-neutral capacities of these provinces was level II, which were poor. Economic development leads to the destruction of the resources and environment. The evaluation values of the driving, pressure, and state subsystems were low. The provincial CCCEIs of Qinghai and Inner Mongolia were the first to attain level IV in 2017, which indicated that their carbon-neutral capacities were good. In 2019, the provincial CCCEIs of Sichuan, Shaanxi, and Gansu were above 1.1, i.e., slightly lower than the lower limit of the standard of IV. These provinces entered the initial stage of development with good carbon-neutral capacities. However, owing to the environmental damage and reduced green areas caused by economic development, the carbon-neutral capacities of Shanxi, Ningxia, Henan, and Shandong are relatively low. In 2019, their provincial CCCEIs were higher than 0.95, but lower than 1.1, thereby indicating their middle development stage of standard III. (2) The provinces in the Yellow River basin have significant development potential. During 2008–2019, the carbon-neutral capacities of provinces in the Yellow River basin achieved a grade leap. However, there is huge scope for development beyond the upper limit of standard IV. Furthermore, no province has reached standard I, thereby leaving scope for further development of provinces. Ningxia and Shanxi are important coal carbon bases in China, and the coal energy industry is an important industry. With the development of green energy technology, the carbon-neutral capacities of these provinces will be significantly improved. Shandong and Henan provinces have huge populations. Small cultivated land area per capita, a small proportion of renewable energy power, and a large proportion of secondary industry lead to increased pressure on carbon-neutral capacities in these provinces. Therefore, it is necessary to increase the utilization of new energy, vigorously develop tertiary industry, strengthen carbon-capture technology, and strive to achieve the sustainable development goal of carbon neutrality at the earliest. 5. Conclusion In this study, the DPSIR model was used in the study of carbon-neutral capacity evaluation, the logical relationship among the subsystems of the DPSIR model was explored, and the carbon-neutral capacities of provinces in the Yellow River basin were evaluated. The main conclusions of this study are as follows. A carbon-neutral-capacity-evaluation index system for the Yellow River basin is established based on the DPSIR model. The index system has three levels and 37 indicators. The index system is scientific, complete, and easy to obtain and provides a basic framework for a comprehensive analysis of the causal relationship between carbon neutrality and social and economic activities in the Yellow River basin. The index system is capable of evaluating the impacts of economic and social developments in various provinces on their carbon-neutral capacities, as well as those of positive measures adopted to achieve carbon neutrality. The proposed index system is broad based and can be extended to the carbon-neutral assessments of other river basins. The global entropy method was used to calculate the capability evaluation value of each subsystem of the DPSIR, and the CCCEI model of the Yellow River basin was constructed and a classification standard for the carbon-neutral capability was proposed. This method can be used to perform quantitative analysis on each subsystem of carbon-neutral capacity of each province in the Yellow River basin. It can dynamically describe the evolution trend of the subsystems and objectively measure the level and future development scope of the carbon-neutral capacity of each province. Between 2008 and 2019, the carbon-neutral capacities of the provinces in the Yellow River basin was in a state of rapid improvement and achieved a leap of a rank. However, there is scope for further improvement. Qinghai and Inner Mongolia had a high level of carbon-neutral capacities, and their provincial CCCEIs were greater than 1.2. The carbon neutrality levels in Sichuan, Shaanxi, and Gansu were slightly lower than the lower limit of the carbon neutrality standard IV, entering the initial stage of the development of higher carbon-neutral capacity. The provincial CCCEIs of Shanxi, Ningxia, Henan, and Shandong were higher than 0.95, but lower than 1.1, indicating a middle development stage of standard III. These conclusions provide new leads and bases for the sustainable development of provinces in the Yellow River basin, and serve as a reference value for realizing carbon neutrality at the earliest. In this study, the weights of the DPSIR subsystem layers were determined using an expert evaluation method. Such a subjective evaluation method can be affected by subjective factors, which may cause significant errors in the results. The weight of the index layer was determined using the entropy method. The results obtained by the objective analysis method are all dependent on data, and the results may be difficult to explain. Therefore, we recommend that a combination of subjective and objective analyses be used in future studies to make the results comprehensive and accurate. Declarations Author Contributions: Methodology: J.X. and Z.L. ; validation: Z.L. and K.Z. ; data curation: K.Z. ; draft preparation: H.W. ; writing–original draft: Z.L. and K.Z. ; review and editing: J.X. and H.W. ; supervision: J.X. and H.W. ; project administration: J.X. AND H.W. All authors have read and agreed to the final version of the manuscript. Funding: This research was funded by Research on the Modernization of Rural Governance from the Perspective of Risk Society [grant number 20BGL214], the National Social Science Fund Project and the Impact of China’s Coal Market Changes on the Economic Development of Shaanxi Province and Policy Research [grant number 2015KRM005], Soft Science Program of Shaanxi Province and Study on Energy Environment Economy Comprehensive Accounting and Its Derivatives of Shaanxi Province Based on Green Social Accounting Matrix [grant number 16JZ040], Key Scientific Research Program of Shaanxi Provincial Department of Education, and Shaanxi Social Science Foundation Project [grant number 2021R039] Conflicts of Interest : The authors declare that there are no conflicts of interest regarding the publication of this paper. Acknowledgements The revision of the language was supposed by Elsevier language editing services. The Data Availability statement in the manuscript (before references under separate heading). Data availability The data that support the findings of this study are available from the corresponding author upon reasonable References Bo. D., Hongmei J. "Study on the construction of low carbon transportation index system in Beijing Tianjin Hebei region." railway transportation and economy 43.02 (2021): 44-49. Cao, Y., & Bian, Y. (2021). Improving the ecological environmental performance to achieve carbon neutrality: The application of DPSIR-Improved matter-element extension cloud model. Journal of Environmental Management, 293(1), 112887. Chaofeng. S., Meiting J. "Research on index system of low carbon city based on DPSIR model." ecological economy. 10 (2010): 95-99. Chen, Y., & Zhang, D. (2021). Evaluation and driving factors of city sustainability in Northeast China: An analysis based on interaction among multiple indicators. Sustainable Cities and Society, 67(June 2020), 102721. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 23 Sep, 2022 Reviews received at journal 10 Sep, 2022 Reviews received at journal 07 Sep, 2022 Reviewers agreed at journal 22 Aug, 2022 Reviewers agreed at journal 18 Aug, 2022 Reviewers invited by journal 15 Aug, 2022 Editor assigned by journal 11 Aug, 2022 Editor invited by journal 08 Aug, 2022 Submission checks completed at journal 08 Aug, 2022 First submitted to journal 08 Jul, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1838219","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":127317400,"identity":"d0c83f70-01a6-402a-891c-d3071fce020d","order_by":0,"name":"Jian Xu","email":"","orcid":"","institution":"Xi'an University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Xu","suffix":""},{"id":127317401,"identity":"26ba6bb4-2b64-44c0-84d3-872e7724e67f","order_by":1,"name":"Haiying 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values of provincial carbon-neutral response subsystems in the Yellow River basin during 2008-2019\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-1838219/v1/f04fa9c1592e46f0f3e7a4ab.png"},{"id":25002224,"identity":"1f04fca1-163f-42cf-8130-f9703dbc6f0c","added_by":"auto","created_at":"2022-08-09 18:15:23","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":156122,"visible":true,"origin":"","legend":"\u003cp\u003eProvincial CCCEIs of the Yellow River basin from 2008-2019\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-1838219/v1/05a45034f97d6fd908a2d8ba.png"},{"id":25001096,"identity":"6037490f-9269-4ee6-9125-cce115bfe1dc","added_by":"auto","created_at":"2022-08-09 18:00:23","extension":"png","order_by":8,"title":"Figure 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emissions of greenhouse gases lead to global warming and aggravate problems such as ecological and environmental deterioration and energy crisis. Therefore, more and more countries have switched to developing low-carbon economies as an effective way for sustainable development since the advent of the 21st century. As a special low-carbon development mode, carbon neutrality promotes balancing of carbon emissions and carbon absorption through the innovation and reform of technologies and systems, industrial transformation and upgrading, and promotion and utilization of clean energy. Finally, the coordinated development of the society, economy, environment, and energy is being emphasized. The terrain of the Yellow River basin is high in the west and low in the east. The western region of the basin is covered with snow throughout the year. Its central area is covered with loess; therefore, the soil and water losses are significant in this region. The eastern region of the basin is comprised mainly of the alluvial plain of the Yellow River. The Yellow River basin spans nine provinces and autonomous regions and plays an important role in China's economic and social development and ecological security. Therefore, the objective and comprehensive evaluation of the carbon-neutral development level of the Yellow River basin is of high significance for the sustainable development of the provinces and cities in the Yellow River basin.\u003c/p\u003e \u003cp\u003eThere is hardly any literature on the development level of carbon neutrality. However, the concept and method of the sustainability evaluation system, evaluation of regional low-carbon economy, ecological level, and analysis of carbon emissions are relatively mature. The selection of evaluation indicators is broadly divided into three categories: basic, core, and tendency indicators (which vary based on research objectives). The selection of indicators depends on the individual scholar Li et al. (2020) in their research on low-carbon economy and the development of agricultural modernization, based on the connotation definition of agricultural modernization, introduced an appropriate agricultural low-carbon evaluation index based on the evaluation index system of agricultural modernization in Heilongjiang Province. This index provides a reference for the construction and development of agricultural modernization in Heilongjiang Province from the perspective of a low-carbon economy. Xiang et al. (2019) combined low-carbon indicators and distribution network operation, starting from the low-carbon contribution of the new distribution network technology and combining with the low-carbon elements of each part of the distribution network. The evaluation index for the low-carbon operation of the distribution network is constructed from four evaluation aspects: low-carbon power, low-damage network, load shifting, and terminal to reduce emissions. The analytic hierarchy process (AHP) and anti-entropy method were used to determine the index combination weights by combining subjective and objective empowerment. A fuzzy comprehensive evaluation method of low-carbon operation by constructing a subordinate function and fuzzy comprehensive evaluation model was established. Dong et al. (2021) focused on the characteristics of the transportation industry to study the low-carbon traffic index system. A balanced scorecard card model was used to establish a low-carbon transportation evaluation index system for the Beijing-Tianjin-Hebei region. Next, the key performance indicators were selected to establish the evaluation index system for low-carbon transportation construction in the Beijing-Tianjin-Hebei region. Shao et al. (2010) studied the carbon emission problem in urban construction and development and considered the actual situation of the development of a low-carbon economy in China. A driving-pressure-state-impact-response (DPSIR) model framework was adopted to establish an indicator system for low-carbon urban construction and evaluation. Li et al. (2014) constructed a low-carbon economy evaluation index system for Hebei Province based on the DPSIR framework model. The relevant statistical data obtained using the AHP were analyzed to evaluate and analyze the low-carbon economic development status of Hebei Province from 2005 to 2010. Lu et al.(2019) established a DPSIR-model-based comprehensive evaluation system for urban river sustainable development. By monitoring the ecological indicators during the restoration process of the Nanfei River, the key factors affecting the ecology are identified to provide a complete and reliable perspective for the ecological impact of river restoration. In addition, useful guidance for improving the state of urban and non-urban river ecosystems is provided. Liu et al.(2018) constructed a DPSIR framework for evaluating the sustainability of marine industrial parks and applied it to evaluate the sustainability of the Shishi Marine Biotechnology Park in Fujian Province of China during 2013\u0026ndash;2016. The AHP and entropy method were used to calculate the weights of indicators. The index variable and the evaluation model were transformed uniformly to obtain the normalized sustainability score. Chen et al.(2020) collected 21 indicators\u0026mdash;spanning the economic, social, and environmental sustainability levels\u0026mdash;to develop an indicator system. Furthermore, they proposed an integrated TOPSIS-ORM approach to evaluate the sustainability level of shrinking cities in the northeast based on the interactions among various indicators. Zhao et al.(2021) constructed an evaluation index for 35 major cities in China. They evaluated the sustainability of the urban ecological resilience network using the ecological network analysis method. Delgado et al.(2021) used the DPSIR models to analyze the ecological status of the Chilean coastal region and its main socio-ecological effects. The results of the family surveys and interviews with local experts and social activists were verified. Malekmohammadi et al.(2017) discussed wetland vulnerability from the perspective of using the DPSIR framework to analyze human and environmental systems. The vulnerability of wetland ecosystem services was also assessed. Threat indicators were characterized based on their significance, severity, and occurrence probability. Guo et al.(2018) established an evaluation index system for Urban low-carbon competitiveness from the perspective of the driving force and resistance. They performed principal component and cluster analyses to evaluate and classify Urban low-carbon competitiveness in the Wuhan metropolitan area. They used a fuzzy analytic hierarchy process to test the results. Furthermore, an objective index analysis was used to identify the obstacles encountered in each city. Finally, the development of Urban low-carbon competitiveness was simulated through system dynamics modeling to determine the problems that exist in the city. Fang et al.(2021) used the DPSIR framework to study the Hainan Island in China. They used the grey correlation analysis and ideal solution similarity sorting technology to perform coupling coordination analysis and study the sustainable development index of the blue economy of the Hainan Island. Duan et al.(2016) used the AHP and entropy weight method to construct an evaluation system for the low-carbon economic development level in Dalian. They used this system to frame a comprehensive evaluation index of the low-carbon economic development level of Dalian from 2005 to 2014. Ma et al.(2017) proposed a new evaluation method for urban green traffic planning. This method was based on the central point triangle whitening weight function and entropy weight analytic hierarchy process. It combined the advantages of the grey evaluation and entropy weight methods and used the grey evaluation method as its central model. The weight of each index was determined using the entropy and AHP composite model. The whitening and comprehensive clustering coefficients of each index were calculated using the central point triangle whitening weight function, which overcomes the subjectivity of the traditional method in determining the weight vector and improves the scientific basis for the evaluation by combining qualitative and quantitative system analyses. Fabianek et al.(2020) adapted the AHP based on economic and ecological standards to perform multi-standard decision analysis and proposed an evaluation framework for green power supply for plug-in electric vehicles in Germany. Cheng et al.(2018) selected and analyzed 21 indicators using correlation analysis, fuzzy rough set, and entropy weight method, based on the connotation of regional green competitiveness. They framed a regional green competitiveness index.\u003c/p\u003e \u003cp\u003eThe aforementioned studies discuss the evaluation indicators and evaluation methods for low-carbon and related industries or economies in different regions or provinces. In these studies, region- and industry-specific parameters are factored in while framing the index. Furthermore, the situation corresponding to an index is relatively simple. However, such specific and relatively simple indexes and evaluations methods are not suitable for evaluating multi-provinces river basins and heterogeneous regions because of their many and diverse characteristics. Li et al.(2012) studied the ecological safety index system of the basin and proposed an improved DPSIR model by including five types of indicators of the basin ecological safety indicators and covering the overall situation of ecological safety disaster change. They also verified the applicability and importance of the improved DPSIR model in basin-scale ecological safety evaluation.\u003c/p\u003e \u003cp\u003eNiu et al.(2021) built an evaluation index, which included six levels and 20 indicators for the carbon neutrality ability evaluation problem, as also the factors affecting it, across provinces. They also proposed an improved TOPSIS method using subjective and objective combinations for analysis and calculation. They report that the use of renewable energy, maintaining ecological environmental quality, and low-carbon technology significantly affecting the carbon-neutrality capacity. Finally, some suggestions are proposed to accelerate carbon neutrality. Liu et al.(2021) empirically analyzed the impact of the three subsystems of ecological civilization on carbon emission intensity using the Chinese provincial panel data for years 2004\u0026ndash;2016 and the Dubin space model based on the Stepat model. Cao et al.(2021) proposed an evaluation model based on an improved object element topology model that combined the DPSIR model, entropy weight method, cloud model, and cloud entropy optimization algorithm. Considering Jiangsu Province as an example, the ecological environment performance of China in 2019 was evaluated using this model. Wei et al.(2019) used pressure-state-response (PSR) methods to identify the factors that affect carbon emissions in China. Empirical studies based on provincial panel data and structural equation models suggest a strong relationship between carbon emissions and the factors that affect them. Jiang et al.(2021) studied the impact of internal and external investment structural changes on global carbon emissions. They used a structural decomposition analysis to decompose global carbon emission changes into six factors, namely, carbon emission intensity, domestic input structure, international investment structure, consumption pattern, consumption, and population. Duan et al.(2021) established DPSIR model by Tapio decoupling analysis method, and the effects of 30 quantitative indexes on water environmental health in Chaohu Lake basin were systematically evaluated. Chen et al.(2019) quantifies final demand by looking at China's provincial CO\u003csub\u003e2\u003c/sub\u003e emissions from different angles. Quantitative study of carbon dioxide emissions by provincial production activities, consumption and income in China. Comparative study of more than 30 provinces in China from different perspectives. The results show that production emits more carbon dioxide than consumption and income in highly industrialized provinces. In the developed eastern coastal provinces and resource-rich provinces, the policy focuses on the demand side and the supply side.\u003c/p\u003e \u003cp\u003eTo sum up, most of the research objects of low-carbon development have been urban areas or specific industries. However, research on the evaluation of low-carbon development levels in river basins and multi-provincial or heterogeneous regions is rare. In addition, most of the research focuses on the current low-carbon economy. Considering the vision and goal of carbon neutrality, few studies have integrated the concept of carbon neutrality into research to evaluate the carbon-neutral development level. Most of the current evaluation indicators for the low-carbon development level are common for carbon emissions and carbon absorption. However, no evaluation indicators have been established specifically for carbon transmission. Research suggests that the DPSIR model can represent the concept and structure of a composite system effectively, and it can be used to evaluate low-carbon or carbon-neutral development levels in many river basins and/or across provinces. Therefore, this study extends the present research to develop the DPSIR analysis model for the evaluation of carbon-neutrality capability. An ecological evaluation index is established based on the actual situation in respect of carbon emissions, carbon absorption, economy, population, technology, resources, environment, etc. of the Yellow River basin. The driving mechanism, pressure, state, impact, and response analyses of the provincial carbon-neutrality evaluation system in the Yellow River basin and other indicators were improved. Combined with the data of social and economic statistics, the AHP and entropy weight methods were used to evaluate the carbon-neutrality performance of nine provinces in the Yellow River basin. The collected data were used to verify the applicability and significance of the improved DPSIR model for the evaluation of carbon-neutral capacity in the evaluation of the carbon-neutral development level of the Yellow River basin to provide a theoretical reference for its sustainable development.\u003c/p\u003e"},{"header":"2. Construction Of Provincial Carbon-neutral Capacity Evaluation Index System For The Yellow River Basin","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 DPSIR model\u003c/h2\u003e \u003cp\u003eThe driving-pressure-state-impact-response (DPSIR) model has evolved from the pressure-state-response (PSR) model proposed by the Organization for Economic Cooperation and Development and the driving-pressure-response (DPR) model proposed by the United Nations Commission on Sustainable Development. It is comprehensive, systematic, holistic, and flexible. The model can reveal the causal relationship between the environment and economy. The DPSIR model divides the system into five factors: driving, pressure, status, impact, and response. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the DPSIR concept model for the Yellow River basin and reflects the interaction process of carbon-neutral influencing factors. Driving (D) represents the driving factors of carbon-neutral capacity caused by provincial economic development in the Yellow River Basin and pressure (P) refers to the pressure of the economic development activities on the carbon-neutral capacity of the basin. State (S) is the state level of the provincial carbon-neutral capacity under pressure. Influence (I) is the feedback result and impact of various state levels on the provincial carbon- neutral capacity in the Yellow River Basin. Response (R) refers to various positive measures and countermeasures adopted to enhance the carbon-neutral capacity of provinces in the basin.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Construction of carbon-neutral-capacity evaluation index system\u003c/h2\u003e \u003cp\u003eTo objectively and scientifically evaluate the carbon-neutral capacity of the Yellow River basin, this study follows the principles of the DPSIR model: science, system, compatibility, regional, hierarchy, operability, and regional economic and social development. The evaluation index system includes three levels: the target layer, subsystem layer, and index layer. The determined index layer comprises 37 specific indicators. It determines its attributes as positive (+) or negative indices (-) for the index characteristics. The specific carbon-neutral-capacity evaluation index system is listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCarbon-neutral-capacity evaluation index system for the Yellow River basin\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget layer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubsystem layer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndex layer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDescription and\u003c/p\u003e \u003cp\u003edescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProperties\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNotes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"36\" rowspan=\"37\"\u003e \u003cp\u003eEvaluation of provincial carbon neutral capacity in the Yellow River Basin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eDriving\u003c/p\u003e \u003cp\u003e(D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGDP per capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ed\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMeasure people's living standard (Yuan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNiu et al.2021.\u003c/p\u003e \u003cp\u003eZhang et al.2019.\u003c/p\u003e \u003cp\u003eShao et al.2010.\u003c/p\u003e \u003cp\u003eLi et al.2014.\u003c/p\u003e \u003cp\u003eLu et al.2019.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGDP per capita\u0026nbsp;growth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ed\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMeasure the growth rate of regional economic growth\u0026nbsp;(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNiu et al.2021.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProvincial Population\u0026nbsp;growth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ed\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMeasure the regional population growth rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNiu et al.2021.\u003c/p\u003e \u003cp\u003eZhang et al.2019.\u003c/p\u003e \u003cp\u003eShao et al.2010.\u003c/p\u003e \u003cp\u003eLu et al.2019.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProvincial Permanent population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ed\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCharacterizing the provincial population distribution (10,000 people)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNiu et al.,2021.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrbanization level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ed\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThe process and degree of population aggregation toward the city (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFang et al.2021.\u003c/p\u003e \u003cp\u003eNiu et al.2021.\u003c/p\u003e \u003cp\u003eShao et al.2010.\u003c/p\u003e \u003cp\u003eLi et al.2014.\u003c/p\u003e \u003cp\u003eLu et al.2019.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndustrial output value\u0026nbsp;growth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ed\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndustrial production growth degree (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFang et al.2021.\u003c/p\u003e \u003cp\u003eNiu et al.2021.\u003c/p\u003e \u003cp\u003eLi et al.2016.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApproved patents ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ed\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRegional scientific and technological innovation capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFang et al.2021.\u003c/p\u003e \u003cp\u003eLi et al.2014.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAfforestation areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ed\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMeasure the ecological improvement capacity of the area (ha/person)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFang et al.2021.\u003c/p\u003e \u003cp\u003eLu et al.2019.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003ePressure\u003c/p\u003e \u003cp\u003e(P)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnergy consumption per capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCapacity to consume energy per person (tons of standard coal/person)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFang et al.2021\u003c/p\u003e \u003cp\u003eNiu et al.2021.\u003c/p\u003e \u003cp\u003eJiang et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarbon emissions per capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal carbon emissions per person (ton/person)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCao et al.2021.\u003c/p\u003e \u003cp\u003eWei et al.2019.\u003c/p\u003e \u003cp\u003eJiang et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCars\u0026nbsp;per capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMeasure traffic carbon pollution (vehicle/person)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFang et al.2021\u003c/p\u003e \u003cp\u003eNiu et al.2021.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage annual heating days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eImpact of heating (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWei et al.2019.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eliving area\u0026nbsp;per capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eImpact of construction industry (m \u003csup\u003e2\u003c/sup\u003e/person)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFang et al.2021\u003c/p\u003e \u003cp\u003eNiu et al.2021.\u003c/p\u003e \u003cp\u003eWei et al.2019.\u003c/p\u003e \u003cp\u003eJiang et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003cp\u003e(S)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eproportion of renewable energy power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003es\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMeasure the regional dependence on traditional energy generation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFang et al.2021\u003c/p\u003e \u003cp\u003eNiu et al.,2021.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarbon absorption to carbon emission ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003es\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal regional carbon absorption/total carbon emissions (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWei et al.2019.\u003c/p\u003e \u003cp\u003eJiang et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGDP proportion of the secondary industry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003es\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGDP of processing and manufacturing industry/GDP of region (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFang et al.2021\u003c/p\u003e \u003cp\u003eNiu et al.2021.\u003c/p\u003e \u003cp\u003eCao et al.2021.\u003c/p\u003e \u003cp\u003eJiang et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion of national hygiene cities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003es\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThe province was selected as National Health City/all cities in the province (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZhang et al.2019.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGDP proportion of the tertiary industry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003es\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eService sector GDP gross value/regional GDP gross value (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFang et al.2021\u003c/p\u003e \u003cp\u003eNiu et al.,2021.\u003c/p\u003e \u003cp\u003eCao et al.2021.\u003c/p\u003e \u003cp\u003eJiang et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCultivated land area\u0026nbsp;per capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003es\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePer capita cultivated land area\u0026nbsp;(mu / person)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNiu et al.2021.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegional air qualified rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003es\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNumber of days the air quality meets the standard/365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNiu et al.2021.\u003c/p\u003e \u003cp\u003eCao et al.2021.\u003c/p\u003e \u003cp\u003eJiang et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual average\u0026nbsp;flow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003es\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eliquid volume flowing through a section (m\u003csup\u003e3\u003c/sup\u003e/ s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZhang et al.2019.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWater qualified rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003es\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNumber of days of Yellow River / (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZhang et al.2019.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eImpact\u003c/p\u003e \u003cp\u003e(I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual average temperature change rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ei\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMeasure the stability of the regional temperature (℃)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWei et al.2019.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarbon dioxide concentration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ei\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRegional air CO\u003csub\u003e2\u003c/sub\u003e contains (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNiu et al.2021.\u003c/p\u003e \u003cp\u003eWei et al.2019.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAir pollution index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ei\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMeasures the quality of regional air quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNiu et al.2021.\u003c/p\u003e \u003cp\u003eWei et al.2019.\u003c/p\u003e \u003cp\u003eJiang et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion of soil\u0026nbsp;erosion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ei\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProvincial soil and soil loss area/provincial total land area (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZhang et al.2019.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComprehensive pollution index of water quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ei\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThe method of evaluating the water environmental quality is divided into six levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNiu et al.2021.\u003c/p\u003e \u003cp\u003eZhang et al.2019.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWaste water discharge volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ei\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal annual wastewater discharge (ton/year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFang et al.2021.\u003c/p\u003e \u003cp\u003eNiu et al.2021.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eResponse\u003c/p\u003e \u003cp\u003e(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion of photovoltaic power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSolar Power Generation / Total Power Generation: (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFang et al.2021.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of the forestry and grass coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMeasure the ability of the natural environment to absorb carbon absorption (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFang et al.2021\u003c/p\u003e \u003cp\u003eNiu et al.2021.\u003c/p\u003e \u003cp\u003eZhang et al.,2019.\u003c/p\u003e \u003cp\u003eWei et al.2019.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePublic transport travel ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEnvironmental impact of carbon emissions from traffic (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFang et al.2021\u003c/p\u003e \u003cp\u003eWei et al.2019.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion of wind power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWind power generation/Total power generation: (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFang et al.2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComprehensive utilization percentage of industrial solid waste\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eComprehensive utilization of industrial solid waste accounts for industrial solid waste production(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFang et al.2021\u003c/p\u003e \u003cp\u003eNiu et al.2021.\u003c/p\u003e \u003cp\u003eWei et al.2019.\u003c/p\u003e \u003cp\u003eJiang et al.2021.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCentralized treatment rate of urban sewage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage of sewage treated by urban centralized sewage treatment plant and urban sewage discharge (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWei et al.2019.\u003c/p\u003e \u003cp\u003eJiang et al.2021.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow-carbon economic development plan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGovernment response to carbon neutrality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNiu et al.2021.\u003c/p\u003e \u003cp\u003eWei et al.2019.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban road areas per capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReacting the congestion of urban traffic (m\u003csup\u003e2\u003c/sup\u003e/ person)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWei et al.2019.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHydropower station\u0026nbsp;capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMeasure the dependence on new energy generation (GW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZhang et al.2019.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Evaluation Of Provincial Carbon-neutral Capacity Level In The Yellow River Basin","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data source and collection methodology\u003c/h2\u003e \u003cp\u003eThe data on economic and social development were mainly sourced from China Statistical Yearbooks from 2008 to 2019 and included data for nine Yellow River basin provinces. The data on the use of resources were sourced mainly from the China Energy Statistical Yearbooks from 2008 to 2019. The data on environmental quality and environmental governance were sourced mainly from the 2008\u0026ndash;2019 China Environmental Statistics Yearbooks, China Bulletin on the Status of the Marine Environment and the Department of Ecology and Environment of the nine Yellow River Basin provinces. The indicator data on per capita carbon emission, annual average temperature change rate, GDP proportion of secondary industry, tertiary industry GDP proportion, and provincial health cities were calculating using their respective formulas from the index description and explanation given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Evaluation-index weight determination based on global entropy value method\u003c/h2\u003e \u003cp\u003eAfter the evaluation index system was established, an appropriate evaluation method was selected to evaluate the development level of the system. Commonly used comprehensive evaluation methods include hierarchical analysis, fuzzy comprehensive evaluation, main component analysis, factor analysis, and entropy methods. The entropy method is an objective empowerment method that calculates the degree of numerical dispersion among the indicators. However, the traditional entropy method can handle only cross-sectional data. It cannot handle the panel data of multi-index system spanning years. Therefore, this study used the global entropy method to determine the weight of the evaluation index system for the provincial carbon-neutral capacity of the Yellow River basin.\u003c/p\u003e \u003cp\u003e(1) Standardized processing of index data Because the original data of the N index may have different units and dimensions, these data must be standardized to obtain standardized data \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{z}}_{{\\alpha }\\text{i}\\text{j}}\\)\u003c/span\u003e\u003c/span\u003e:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${\\text{z}}_{{\\alpha }\\text{i}\\text{j}} = \\left\\{\\begin{array}{c}\\frac{ {\\text{x}}_{{\\alpha }\\text{i}\\text{j}}-{\\text{x}}_{\\text{m}\\text{i}\\text{n}}}{{\\text{x}}_{\\text{m}\\text{a}\\text{x}}-{\\text{x}}_{\\text{m}\\text{i}\\text{n}}} forward indicators\\\\ \\frac{ {\\text{x}}_{\\text{m}\\text{a}\\text{x}}-{\\text{x}}_{{\\alpha }\\text{i}\\text{j}}}{{\\text{x}}_{\\text{m}\\text{a}\\text{x}}-{\\text{x}}_{\\text{m}\\text{i}\\text{n}}} Negative indicators\\end{array}\\right.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\text{x}}_{\\text{m}\\text{i}\\text{n}} \\text{a}\\text{n}\\text{d} \\text{x}}_{\\text{m}\\text{a}\\text{x}}\\)\u003c/span\u003e\u003c/span\u003e represent the minimum and maximum of an index in all years and in all provinces, respectively; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\text{x}}_{{\\alpha }\\text{i}\\text{j}} \\text{a}\\text{n}\\text{d} \\text{z}}_{{\\alpha }\\text{i}\\text{j}}\\)\u003c/span\u003e\u003c/span\u003e, respectively, represent the values of item α of the j\u003csup\u003eth\u003c/sup\u003e province before and after standardization in year i.\u003c/p\u003e \u003cp\u003e(2) Normalization of index standardization data:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${\\text{y}}_{{\\alpha }\\text{i}\\text{j}}=\\frac{{\\text{z}}_{{\\alpha }\\text{i}\\text{j}}}{\\sum _{\\text{i}=1}^{\\text{m}}\\sum _{\\text{j}=1}^{\\text{n}}{\\text{z}}_{{\\alpha }\\text{i}\\text{j}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e(3) Calculate the information entropy of each index \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{E}}_{{\\alpha }}\\)\u003c/span\u003e\u003c/span\u003e:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${\\text{E}}_{{\\alpha }}=-\\text{k}\\sum _{\\text{i}=1}^{\\text{m}}\\sum _{\\text{j}=1}^{\\text{n}}{\\text{y}}_{{\\alpha }\\text{i}\\text{j}}\\text{l}\\text{n}{\\text{y}}_{{\\alpha }\\text{i}\\text{j}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{k}=1/\\text{l}\\text{n}(\\text{m}\\times \\text{n}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\()\\)\u003c/span\u003e\u003c/span\u003e. When \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{y}}_{{\\alpha }\\text{i}\\text{j}}=0\\)\u003c/span\u003e\u003c/span\u003e, set \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{y}}_{{\\alpha }\\text{i}\\text{j}}\\text{l}\\text{n}{\\text{y}}_{{\\alpha }\\text{i}\\text{j}}\\)\u003c/span\u003e\u003c/span\u003e=0.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCalculate the redundancy of each index \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{D}}_{{\\alpha }}\\)\u003c/span\u003e\u003c/span\u003e:\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cdiv id=\"Equ4\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$${\\text{D}}_{{\\alpha }}=1-{\\text{E}}_{{\\alpha }}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCalculate the weights of each index \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{w}}_{{\\alpha }}\\)\u003c/span\u003e\u003c/span\u003e:\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cdiv id=\"Equ5\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$${\\text{w}}_{{\\alpha }}=\\frac{{\\text{D}}_{{\\alpha }}}{\\sum _{{\\alpha }=1}^{\\text{N}}{\\text{D}}_{{\\alpha }}}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 DPSIR model subsystem level evaluation\u003c/h2\u003e \u003cp\u003eThe DPSIR model subsystem layer evaluation formula is as follow:\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$${\\text{S}}_{\\text{d}\\text{p}\\text{s}\\text{i}\\text{r}}=\\sum _{{\\alpha }=1}^{\\text{N}}{\\text{w}}_{{\\alpha }}\\times {\\text{y}}_{{\\alpha }\\text{i}\\text{j}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{S}}_{\\text{d}\\text{p}\\text{s}\\text{i}\\text{r}}\\)\u003c/span\u003e\u003c/span\u003e is the subsystem layer evaluation value of the DPSIR model. The five subsystem evaluation values are as follows: driving subsystem evaluation value (S\u003csub\u003ed\u003c/sub\u003e), pressure subsystem evaluation value (S\u003csub\u003ep\u003c/sub\u003e.), state subsystem evaluation value (S\u003csub\u003es\u003c/sub\u003e), impact subsystem evaluation value (S\u003csub\u003ei\u003c/sub\u003e.), and response subsystem evaluation value (S\u003csub\u003er\u003c/sub\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Comprehensive evaluation index and capacity level of provincial carbon-neutral capacity of the Yellow River basin\u003c/h2\u003e \u003cp\u003eThe weight (W\u003csub\u003edpsir\u003c/sub\u003e) of the DPSIR subsystem layer was determined using the expert evaluation method. The five subsystem weights were as follows: driving (w\u003csub\u003ed\u003c/sub\u003e), pressure (W\u003csub\u003ep\u003c/sub\u003e.), state (W\u003csub\u003es\u003c/sub\u003e), impact (W\u003csub\u003ei\u003c/sub\u003e.) and response (W\u003csub\u003er\u003c/sub\u003e) weights.\u003c/p\u003e \u003cp\u003eThe carbon-neutral capacities comprehensive evaluation Index (CCCEI) was obtained as\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$\\text{C}\\text{C}\\text{C}\\text{E}\\text{I}={\\text{S}}_{\\text{d}}\\times +{\\text{S}}_{\\text{p} }\\times {\\text{W}}_{\\text{p}}+{\\text{S}}_{\\text{s}}\\times {\\text{W}}_{\\text{s}}+{\\text{S}}_{\\text{i}}\\times {\\text{W}}_{\\text{i}}+{\\text{S}}_{\\text{r}}\\times {\\text{W}}_{\\text{r}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eConsidering the score of the comprehensive evaluation index of the provincial carbon-neutral capacity of the Yellow River basin and referring to the relevant comprehensive index classification method at home and abroad and the actual situation of this study, the grading standards for establishing provincial carbon-neutral、 capacities in the Yellow River basin are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification of carbon-neutral capacities in the Yellow River basin\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCCEI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarbon-neutral capacity level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(0.4, 0.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(0.8, 1.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(1.2, 1.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅣ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅤ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Empirical Analysis","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e4.1 Subsystem layer and index layer weights calculation\u003c/h2\u003e\n\u003cp\u003eThe DPSIR subsystem layers\u0026rsquo; weights were calculated by five experts by using expert evaluation method. Next, the global entropy method was used to process 37 index datasets from nine provinces located in the Yellow River basin. The calculation results are listed in Table\u0026nbsp;3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;3. Weights of the provincial carbon-neutral capacity evaluation index system\u0026nbsp;in the Yellow River basin\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTarget layer\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSubsystem layer\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIndex layer\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eWeight\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"37\" align=\"left\"\u003e\n\u003cp\u003eEvaluation of provincial carbon neutral capacity in the Yellow River Basin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"8\" align=\"left\"\u003e\n\u003cp\u003eDriving( D)\u003c/p\u003e\n\u003cp\u003e0.2021\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDP per capita\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ed\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1069\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDP per capita growth rate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ed\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1339\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProvincial Population growth rate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ed\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0119\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProvincial Permanent population\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ed\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1343\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUrbanization level\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ed\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0637\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIndustrial output value growth rate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ed\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1442\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eApproved patents ratio\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ed\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1085\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAfforestation area\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ed\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.2966\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003ePressure(P)\u003c/p\u003e\n\u003cp\u003e0.2071\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEnergy consumption per capita\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1912\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCarbon emissions per capita\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.2639\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCar ownership per capita\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1186\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAverage annual heating days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1374\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eliving area\u0026nbsp;per capita\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.2889\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"9\" align=\"left\"\u003e\n\u003cp\u003eStatus (S)\u003c/p\u003e\n\u003cp\u003e0.2093\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProportion of renewable energy power\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003es\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.2091\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCarbon absorption to carbon emission ratio\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003es\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.2142\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDP proportion of the secondary industry\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003es\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1357\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProportion of national hygiene cities\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003es\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1275\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDP proportion of the tertiary industry\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003es\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0515\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCultivated land area per capita\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003es\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1014\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegional air qualified rate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003es\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0480\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAnnual average flow\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003es\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0603\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWater qualified rate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003es\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0523\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eImpact (I)\u003c/p\u003e\n\u003cp\u003e0.1654\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAnnual average temperature change rate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ei\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1644\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e concentration\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ei\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.2187\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAir pollution index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ei\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.2730\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProportion of soil erosion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ei\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1387\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eComprehensive pollution index of water quality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ei\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0863\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWaste water discharge volume\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ei\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1190\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"9\" align=\"left\"\u003e\n\u003cp\u003eResponse (R)\u003c/p\u003e\n\u003cp\u003e0.2161\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProportion of photovoltaic power\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003er\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1399\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePercentage of the forestry and grass coverage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003er\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1942\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePublic transport mobility and travel ratio\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003er\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0414\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProportion of wind power\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003er\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.2067\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eComprehensive utilization percentage of industrial solid waste\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003er\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0269\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCentralized treatment rate of urban sewage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003er\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0156\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow-carbon economic development plan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003er\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1682\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUrban road areas per capita\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003er\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0352\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePower generation capacity of the Hydropower station\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003er\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1719\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e4.2 Analysis of factors affecting provincial carbon-neutral capacity development levels in the Yellow River basin\u003c/h2\u003e\n\u003cp\u003eThe DPSIR subsystem was thoroughly analyzed by comparing the evaluation values of the carbon-neutral capacity DPSIR subsystem of the nine provinces in the Yellow River basin from 2008 to 2019 and combining the characteristics of resources and environment and the specific characteristics of the basin.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(1) Driving subsystem\u003c/p\u003e\n\u003cp\u003eThe evaluation values of the carbon-neutral capacity driving subsystems in nine provinces in the Yellow River basin generally exhibited an upward trend, as depicted in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Among the provinces, Inner Mongolia has an extensive and large forest coverage area; therefore, its carbon-neutral driving subsystem evaluation value is at a relatively high level. Sichuan, Shaanxi, and Shandong have the largest GDP per capita growth rate, and relative scientific research patent technological achievements have gradually increased. Economic and technological developments provide sufficient support for carbon neutrality. The evaluation value of the carbon-neutral driving subsystem increased steadily, with a small annual growth in other provinces. The growth rate of various carbon-neutral driving indices slowed. Overall, the carbon-neutral capacity driving subsystem in nine provinces in the Yellow River basin has been maintained at a relatively high level and has achieved good driving results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(2) Pressure subsystem\u003c/p\u003e\n\u003cp\u003eAlthough the evaluation values of carbon-neutral pressure subsystems in the nine provinces of the Yellow River basin decreased slightly in some years during 2008\u0026ndash;2019, the overall trend exhibited a small increase, as depicted in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e Carbon emissions per capita increased slightly. However, with economic development and technological progress, the energy consumed per unit economic growth was significantly reduced. Overall, the pressure subsystem evaluation values exhibited an increasing trend. Because the Yellow River basin provinces are located in the north, the heating requirements and people's living conditions put an increased pressure on the resources and environment. This leads to the instability of the pressure subsystem evaluation values and inhibits the growth of the pressure subsystem evaluation values. Although the provinces have actively strengthened the idea of a low-carbon economy in recent years and gradually increased their investment in realizing the carbon-neutral development goal, the pressure problems facing carbon neutrality are difficult to improve significantly on a short timeframe.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(3) State subsystem\u003c/p\u003e\n\u003cp\u003eFigure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e exhibits the state subsystem evolution trend of the provincial carbon-neutral capacity in the Yellow River basin during 2008\u0026ndash;2019. Under the dual action of the driving and pressure, the evaluation value of the carbon-neutral state subsystem in Henan, Shandong, and Shanxi was at a low level. In the face of the environmental problems brought about by the provincial economic development in the Yellow River basin, the effectiveness of the measures adopted is not obvious, thereby resulting in pressure on environmental resources. Existing governance measures cannot effectively improve the state of the resources and environment. The rationality of an industrial structure determines its level of economic quality. Industrial transformation and upgrading are conducive to improving the quality of economy. When compared with the tertiary industry, the secondary industry in its growth stage has the characteristics of a high cost of unit economic growth. Inner Mongolia, Qinghai, and Gansu have relatively small proportions of secondary industry and large proportions of renewable energy in power generation. Therefore, these provinces maintain a good carbon-neutral state. Recently, Sichuan, Ningxia, and Shaanxi have actively developed emerging industries, strengthened ecological and environmental protection in the Yellow River basin, and formulated and implemented low-carbon development plans. These measures have effectively eliminated some of the negative impacts of development activities. Presently, the carbon-neutrality states in most provinces in the Yellow River basin are alarming, and the resource and environmental problems are evident. To improve their carbon-neutral states, the resources and environment of the Yellow River basin must be further and rationally utilized, treated, and protected. In general, China's carbon-neutral state subsystem in the Yellow River basin requires to be further improved.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(4) Impact subsystem\u003c/p\u003e\n\u003cp\u003eFigure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e depicts the impact subsystem evolution trends of the provincial carbon-neutral capacities in the Yellow River basin from 2008 to 2019. From the perspective of environmental and resource impacts of provinces in the Yellow River basin, certain fluctuations are evident in the evaluation values of the carbon-neutral impact subsystems of provinces. Although the rates of increase in the temperature in all provinces are negative, the temperatures in all provinces in 2019 exhibited a significant increase compared with those of 2008. The Yellow River basin suffers from serious soil and water losses. Therefore, it has negatively impacted the trend of the carbon-neutral impact subsystem evaluation values. However, the characteristics of low carbon dioxide emissions and a low air comprehensive pollution index in Qinghai and Inner Mongolia ensure that the carbon-neutral impact subsystem in this region remains at a high level. Shaanxi, Henan, Sichuan, Gansu, Shanxi, Ningxia, and Shandong were affected negatively by the provincial economic development. Owing to the excessive development of resources and emissions and wastewater in the process of economic development in each region, the evaluation value of the impact subsystem has decreased to a certain extent, thereby hindering any improvement of the evaluation value of the impact subsystem. Therefore, to mitigate the negative impact of the provincial economic development in the Yellow River basin, it is necessary to establish a reasonable resource and environment utilization mechanism and strictly control the emissions of industrial pollutants.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(5) Response subsystem\u003c/p\u003e\n\u003cp\u003eFigure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e depicts the response subsystem evolution trend of the provincial carbon-neutral capacity in the Yellow River basin from 2008 to 2019. Carbon-neutral driving indices and pressure indices varied across provinces, and the response degree of the measures adopted varied. However, the evaluation values of the response subsystem rapidly and steadily improved after wavy increases and decreases in the early stage. In Henan, Shaanxi, Shanxi, Shandong, and Sichuan, the government organized afforestation drives and invested efforts in scientific and technological research and development to improve the utilization rate of the industrial waste and urban sewage and achieved good results. Ningxia and Gansu promoted public transport to implement a low-carbon economic development plan and promote the improvement of the evaluation value of the provincial carbon-neutral response subsystem in the Yellow River basin.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e4.3 Comprehensive evaluation and analysis of provincial carbon-neutral capacities in the Yellow River basin\u003c/h2\u003e\n\u003cp\u003eThe provincial CCCEI in the Yellow River basin from 2008 to 2019 is depicted in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. A radar map of provincial CCCEI is presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. The following features can be observed.\u003c/p\u003e\n\u003cp\u003e(1) Overall, the provincial carbon-neutral capacities in the Yellow River basin were gradually enhanced.\u003c/p\u003e\n\u003cp\u003eDuring 2008\u0026ndash;2019, the provincial carbon-neutral capacities in the Yellow River basin were continuously enhanced, and the CCCEI exhibited an upward trend. Since 2013, the concepts of low-carbon economy and sustainable development have attracted wide attention from the society and governments at all levels. In 2013, \u003cem\u003ethe State Council on Printing and Distributing the National Sustainable Development Plan for Resource-based Cities (2013\u0026ndash;2020)\u003c/em\u003e was issued. The development plan for economic sustainable development in the next seven years was introduced. From 2013 to 2019, nine provinces responded positively to state calls. Governments at all levels need to rationally develop and use environmental resources and strengthen environmental governance and protection in accordance with relevant work arrangements. The strong development of the response subsystem and gradual comprehensive and stable implementation of the regulation measures improved the quality of economic development, the pressure and negative impact on the ecological environment of the Yellow River basin decreased continuously, and the CCCEI increased rapidly.\u003c/p\u003e\n\u003cp\u003eHowever, driven by the ever increasing population and economic aggregate, the increases in the coal-dominated industrial energy consumption structure and cars per capita ensure that the evaluation values of the pressure and impact subsystems keeps fluctuating, thereby impeding the development of provincial CCCEI in the Yellow River basin.\u003c/p\u003e\n\u003cp\u003e(2) The development of carbon-neutral capacity in the Yellow River basin varies.\u003c/p\u003e\n\u003cp\u003eQinghai and Inner Mongolia have relatively good ecological environments, large vegetation coverage areas, and underdeveloped economies. Therefore, the provincial CCCEIs were above 0.8 in 2008.\u003c/p\u003e\n\u003cp\u003eFrom 2008 to 2012, the provincial CCCEI s of Henan, Shanxi, Ningxia, and Shandong were less than 0.8. The overall levels of the carbon-neutral capacities of these provinces was level II, which were poor. Economic development leads to the destruction of the resources and environment. The evaluation values of the driving, pressure, and state subsystems were low.\u003c/p\u003e\n\u003cp\u003eThe provincial CCCEIs of Qinghai and Inner Mongolia were the first to attain level IV in 2017, which indicated that their carbon-neutral capacities were good. In 2019, the provincial CCCEIs of Sichuan, Shaanxi, and Gansu were above 1.1, i.e., slightly lower than the lower limit of the standard of IV. These provinces entered the initial stage of development with good carbon-neutral capacities. However, owing to the environmental damage and reduced green areas caused by economic development, the carbon-neutral capacities of Shanxi, Ningxia, Henan, and Shandong are relatively low. In 2019, their provincial CCCEIs were higher than 0.95, but lower than 1.1, thereby indicating their middle development stage of standard III.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(2) The provinces in the Yellow River basin have significant development potential.\u003c/p\u003e\n\u003cp\u003eDuring 2008\u0026ndash;2019, the carbon-neutral capacities of provinces in the Yellow River basin achieved a grade leap. However, there is huge scope for development beyond the upper limit of standard IV. Furthermore, no province has reached standard I, thereby leaving scope for further development of provinces. Ningxia and Shanxi are important coal carbon bases in China, and the coal energy industry is an important industry. With the development of green energy technology, the carbon-neutral capacities of these provinces will be significantly improved. Shandong and Henan provinces have huge populations. Small cultivated land area per capita, a small proportion of renewable energy power, and a large proportion of secondary industry lead to increased pressure on carbon-neutral capacities in these provinces. Therefore, it is necessary to increase the utilization of new energy, vigorously develop tertiary industry, strengthen carbon-capture technology, and strive to achieve the sustainable development goal of carbon neutrality at the earliest.\u003c/p\u003e \n\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this study, the DPSIR model was used in the study of carbon-neutral capacity evaluation, the logical relationship among the subsystems of the DPSIR model was explored, and the carbon-neutral capacities of provinces in the Yellow River basin were evaluated. The main conclusions of this study are as follows.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eA carbon-neutral-capacity-evaluation index system for the Yellow River basin is established based on the DPSIR model. The index system has three levels and 37 indicators. The index system is scientific, complete, and easy to obtain and provides a basic framework for a comprehensive analysis of the causal relationship between carbon neutrality and social and economic activities in the Yellow River basin. The index system is capable of evaluating the impacts of economic and social developments in various provinces on their carbon-neutral capacities, as well as those of positive measures adopted to achieve carbon neutrality. The proposed index system is broad based and can be extended to the carbon-neutral assessments of other river basins.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe global entropy method was used to calculate the capability evaluation value of each subsystem of the DPSIR, and the CCCEI model of the Yellow River basin was constructed and a classification standard for the carbon-neutral capability was proposed. This method can be used to perform quantitative analysis on each subsystem of carbon-neutral capacity of each province in the Yellow River basin. It can dynamically describe the evolution trend of the subsystems and objectively measure the level and future development scope of the carbon-neutral capacity of each province.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBetween 2008 and 2019, the carbon-neutral capacities of the provinces in the Yellow River basin was in a state of rapid improvement and achieved a leap of a rank. However, there is scope for further improvement. Qinghai and Inner Mongolia had a high level of carbon-neutral capacities, and their provincial CCCEIs were greater than 1.2. The carbon neutrality levels in Sichuan, Shaanxi, and Gansu were slightly lower than the lower limit of the carbon neutrality standard IV, entering the initial stage of the development of higher carbon-neutral capacity. The provincial CCCEIs of Shanxi, Ningxia, Henan, and Shandong were higher than 0.95, but lower than 1.1, indicating a middle development stage of standard III. These conclusions provide new leads and bases for the sustainable development of provinces in the Yellow River basin, and serve as a reference value for realizing carbon neutrality at the earliest.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIn this study, the weights of the DPSIR subsystem layers were determined using an expert evaluation method. Such a subjective evaluation method can be affected by subjective factors, which may cause significant errors in the results. The weight of the index layer was determined using the entropy method. The results obtained by the objective analysis method are all dependent on data, and the results may be difficult to explain. Therefore, we recommend that a combination of subjective and objective analyses be used in future studies to make the results comprehensive and accurate.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions: \u003c/strong\u003eMethodology: J.X. and Z.L. ; validation: Z.L. and K.Z. ; data curation: K.Z. ; draft preparation: H.W. ; writing\u0026ndash;original draft: Z.L. and K.Z. ; review and editing: J.X. and H.W. ; supervision: J.X. and H.W. ; project administration: J.X. AND H.W. All authors have read and agreed to the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research was funded by Research on the Modernization of Rural Governance from the Perspective of Risk Society [grant number 20BGL214], the National Social Science Fund Project and the Impact of China\u0026rsquo;s Coal Market Changes on the Economic Development of Shaanxi Province and Policy Research [grant number 2015KRM005],\u0026nbsp;Soft\u0026nbsp;Science\u0026nbsp;Program\u0026nbsp;of\u0026nbsp;Shaanxi\u0026nbsp;Province and Study on Energy Environment\u0026nbsp;Economy\u0026nbsp;Comprehensive\u0026nbsp;Accounting\u0026nbsp;and\u0026nbsp;Its\u0026nbsp;Derivatives\u0026nbsp;of\u0026nbsp;Shaanxi Province\u0026nbsp;Based\u0026nbsp;on\u0026nbsp;Green\u0026nbsp;Social\u0026nbsp;Accounting\u0026nbsp;Matrix [grant number 16JZ040], Key Scientific\u0026nbsp;Research\u0026nbsp;Program\u0026nbsp;of\u0026nbsp;Shaanxi\u0026nbsp;Provincial\u0026nbsp;Department\u0026nbsp;of\u0026nbsp;Education, and\u0026nbsp;Shaanxi Social Science Foundation Project [grant number 2021R039]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e: The authors declare that there are no conflicts of interest regarding the publication of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe revision of the language was supposed by Elsevier language editing services.\u003c/p\u003e\n\u003cp\u003eThe Data Availability statement in the manuscript (before references under separate heading).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBo. 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Y. \"Study on evaluation index system of watershed ecological security based on DPSIR model.\" Journal of Peking University (NATURAL SCIENCE EDITION) 48.06 (2012): 971-981.\u003c/li\u003e\n\u003cli\u003eZhao, R., Fang, C., Liu, H., \u0026amp; Liu, X. (2021). Evaluating urban ecosystem resilience using the DPSIR framework and the ENA model: A case study of 35 cities in China. Sustainable Cities and Society, 72(May), 102997.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"DPSIR model, carbon-neutral capacities comprehensive evaluation index (CCCEI), carbon-neutral, Yellow River basin, evaluation index system\t ","lastPublishedDoi":"10.21203/rs.3.rs-1838219/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1838219/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Yellow River basin plays an important role in China's economic and social development and ecological security. To study the changes in the trend and driving mechanisms of the carbon-neutral capacity of the Yellow River basin and provide a theoretical reference value for a comprehensive realization of carbon neutrality in China in 2060, the corresponding subsystems based on the driving-force-pressure-state-impact-response (DPSIR) model framework were established. Furthermore, a DPSIR index system, which consisted of 39 factors reflecting the carbon-neutrality capacity and ecological environment state of the Yellow River basin, was proposed. The DPSIR subsystem layers\u0026rsquo; weights were determined using an expert evaluation method. The global entropy method was used to obtain the weights of the 39 indicators, the evaluation model of carbon-neutral capacity was proposed to calculate the comprehensive evaluation value of the provincial carbon-neutral capacities comprehensive evaluation Index (CCCEI) in the Yellow River basin. Our results indicate that, from the perspective of the DPSIR subsystems, the evaluation value of the carbon-neutral capacity driving subsystem in the Yellow River basin exhibited an overall upward trend from 2008 to 2019. However, the evaluation value of the carbon-neutral capacity pressure subsystem decreased slightly in some years, while the overall trend increased marginally. The carbon-neutral capacity status subsystem evaluation value was at a lower level and requires further improvement. The evaluation value of the carbon-neutral capacity impact subsystem had a certain fluctuation, and the evaluation value of the carbon-neutral capacity response subsystem improved rapidly and steadily afterward. The final results indicated that, from 2008 to 2019, the carbon-neutral capacities of the provinces in the Yellow River basin were in a state of rapid development and had achieved a grade leap. However, seven provinces had carbon-neutral capacity levels at Grade III standard in 2019, thereby leaving scope for substantial improvement.\u003c/p\u003e","manuscriptTitle":"Evaluation of Provincial Carbon-neutral Capacities in the Yellow River Basin Using DPSIR","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-09 18:00:21","doi":"10.21203/rs.3.rs-1838219/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-09-23T10:27:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-09-10T08:45:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-09-07T19:43:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"02c3cbe1-4405-41fe-8e48-77f8f143b6b3","date":"2022-08-22T22:57:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2921f63c-e268-4230-bf81-c5d794281ec8","date":"2022-08-18T18:25:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-08-15T15:18:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-08-11T13:27:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2022-08-08T12:34:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-08-08T12:31:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2022-07-08T08:50:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0ceb5c20-6176-4bdc-b275-877faed2dd6b","owner":[],"postedDate":"August 9th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-10-25T12:14:12+00:00","versionOfRecord":[],"versionCreatedAt":"2022-08-09 18:00:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1838219","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1838219","identity":"rs-1838219","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}