Navigating China's carbon neutrality journey: insights from policy instruments and implementation strategies across provincial regions.

China's critical reliance on well-crafted public policies, coupled with the effective execution of central government directives at the local level, drives the achievement of the "dual carbon" goal including the peaking of CO2 emissions and attaining carbon neutrality. Therefore, examining policy records can unveil the holistic strategy for attaining carbon neutrality during the period of peak CO2 emissions; at the same time, it can also highlight the potential obstacles in policy implementation. In this study, we adopt a policy instruments perspective to investigate data related to policies addressing peak CO2 emissions across 29 provincial administrative regions in China. We apply Nvivo12 software to conduct a quantitative literature assessment and content analysis to establish a theoretical framework for the policy process. This framework encompasses dimensions such as political feasibility, regional coordination, attributes of low-carbon initiatives, and policy refinement. Subsequently, we employ the model to carry out a retrospective analysis of policy documents pertaining to peak CO2 emissions in China. Our research findings underscore the pivotal role of political feasibility in shaping policy effectiveness, while also highlighting the facilitative influence of regional coordination, shedding light on the essential synergy between provinces and cities in achieving emissions reduction goals. Similarly, the estimated results highlight the motivating impact of specific attributes within low-carbon initiatives. Moreover, policy enhancements are identified as a critical driver in advancing the path toward carbon neutrality. Consequently, to achieve the objective of carbon neutrality, it is imperative for every province and city to sequentially reach the peak of CO2 emissions. Our research offers a comprehensive "China strategy," providing valuable insights to guide future policy formulation and accelerate progress toward sustainable environmental objectives.

Untangling the causal mechanisms and spatial dynamics of digital financial development's impact on energy intensity: insights from panel data of Chinese provinces.

The prime focus of the present investigation delves into the linkage between digital financial services and energy intensity within the geographic confines of China, utilizing provincial-level panel data spanning from 2011 to 2021. Digital finance has rapidly developed due to changes in information technology, and its role in achieving green transformation, reducing energy consumption, and lowering energy intensity in Chinese society is critical. By conducting empirical analysis utilizing diverse models, we have tested our hypotheses and found that digital finance's improvement can contribute to the reduction in energy intensity at the regional level while still considering endogeneity concerns. This effect is mediated by the promotion of technological innovation and the facilitation of green development in industries. Digital finance's impact on energy intensity is contingent upon resource endowments, such as the level of traditional financial development and the degree of information. Moreover, digital finance's adverse impact on energy intensity becomes more pronounced beyond certain threshold values. However, digital finance can increase energy intensity in neighboring regions through spatial spillover effects. Drawing upon our findings, we recommend bolstering the development of digital finance, augmenting the capability for autonomous innovation, and devising specialized strategies for digital finance advancement to fully harness the potential of digital finance in curbing energy intensity. This study interprets the value of digital finance from the new perspective of energy intensity. By exploring the internal links between digital finance and energy intensity, the study enriches the research results on the impact of digital finance on energy intensity.

Variation of digital economy's effect on carbon emissions: improving energy efficiency and structure for energy conservation and emission reduction.

The significance of accurately assessing the influence of digital economy growth upon reducing emission of carbon in the context of worldwide climate governance cannot be overstated. This is crucial in encouraging low-carbon economic advancement at national level, achieving carbon peak and neutrality as soon as possible, and creating a shared future for humanity. A mediating effect model is established using cross-country panel data from 100 countries, ranging from 1990 to 2019, to assess the influence of digital economy development upon emission of carbon and to explore its underlying mechanism. The study found that: the growth of national emission of carbon can be considerably suppressed by digital economy development, and the reduction of emissions is positively associated to each country's level of economic advancement. Digital economy growth influences regional emission of carbon via intermediary channels like energy structure and efficiency, with energy intensity having a particularly noticeable intermediary impact. The inhibitory influence of digital economy development upon emission of carbon differs among countries with different levels of income, and improvements in energy structure and efficiency can precede to energy savings and emission reduction in both middle- and high-income countries. The above findings offer policy guidance for harmoniously advancing the growth of digital economy and climate management, hastening the low-carbon transformation of national economies, and implementing China's carbon peaking initiative.

Role of agricultural credit guarantee policies in encouraging green agricultural development: farmers' perspectives and responses, and the regulatory function of household capital.

Green agriculture is anticipated to be the leading trend for achieving sustainable and high-quality development in the agricultural sector in the future. The success of credit guarantee policies in promoting green agricultural development is closely linked to the level of participation and response from farmers in securing agricultural credit guarantee loans. We examined how farmers in Xiji, Ningxia, perceive agricultural credit guarantee policies and their involvement in such loans by analyzing 706 survey responses. Our analysis utilized a combination of statistical techniques, including principal component analysis, Heckman's two-stage model, and moderating effect model. The results indicate that out of the 706 surveyed farmers, 29.32% of households (207 households) had knowledge of the agricultural credit guarantee policy. While 66.86% of households (472 households) expressed interest in participating in agricultural credit guarantee loans, only 23.65% of households actually took part or participated multiple times. The overall awareness of the agricultural credit guarantee policy among farmers and their participation rate are both low. An increase in farmers' awareness of the agricultural credit guarantee policy can have a significant impact on their willingness and frequency of participation. The farmer's understanding of the agricultural credit guarantee policy has a significant impact on their decision to participate in credit guarantee loans. However, this effect can vary based on the farmer's income level, household capital, and factors such as social security, personal characteristics, location, and type of household business. To improve the support provided to farmers, it is advised to increase their awareness and understanding of agricultural credit guarantee policies. Furthermore, loan products and services should be personalized according to the capital available to each farmer's household, and the agricultural credit guarantee system and process should be enhanced to provide better assistance.

Investigating the rationale for low-carbon production techniques in agriculture for climate change mitigation and fostering sustainable development via achieving lowcarbon targets.

In China, agricultural activities are a major source of greenhouse gas emissions, ranking second only to another significant source. This presents a significant obstacle to reducing emissions and jeopardizes both the availability of food and the sustainable growth of agriculture. It is primarily the farmers who utilize cultivated land and are thus accountable for the initiation of these emissions. Farmers' role is significant in adopting green and low-carbon (LC) agricultural production practices, and their actions are directly tied to the achievement of the dual goals of carbon reduction. Understanding their motivations for engaging in LC production and the factors that influence their willingness to do so is important for both theory and practice. In this study, data was collected from 260 questionnaires in 13 counties across five major cities in Shaanxi Province. The purpose was to identify factors that impact farmers' motivation and willingness to engage in LC agriculture using linear regression analysis. A structural equation model was constructed to better understand the underlying mechanisms that influence farmers' actions towards LC farming practices. The study's findings indicate that (1) farmers' behavior towards LC production practices is notably impacted by internal motivation based on joy and internal motivation based on responsibility (IMR); (2) IMR has the most pronounced effect on farmers' adoption of LC production practices; (3) the internal motivation based on joy, IMR, behavior attitude, subjective norm, and perceived behavior control are related to each other; and (4) the multi-group analysis of the data indicates that the impact of internal motivation based on joy and IMR on adopting sustainable farming practices may vary among different groups. It is essential to support farmers who have strong intrinsic motivation to engage in sustainable agriculture. Additionally, policymakers must promote positive attitudes towards sustainable farming to achieve the desired environmental (LC) objectives.

Analysis of financial support efficiency and influencing factors of listed seed companies from the perspective of energy consumption and carbon emissions.

The seed industry has a prominent role in strengthening ecological stability and national food security as it provides the basic ground for agriculture sector. In the current research, the effectiveness of financial support provided to listed seed enterprises and its influencing factors from the perspectives of energy consumption and carbon emissions are examined using a three-stage DEA-Tobit model. The dataset for the underlined study variables mainly comes from the financial data published by 32 listed seed enterprises and the China Energy Statistical Yearbook from 2016 to 2021. In order to make the results more accurate, the influence of external environmental factors such as economic development level, total energy consumption, and total carbon emission on listed seed enterprises are excluded. The results revealed that the mean value of financial support efficiency of listed seed enterprises increased significantly after excluding the influence of external environmental and random factors. External environmental factors such as regional energy consumption and carbon dioxide emission played an important role in the process of the financial system supporting the development of listed seed enterprises. The development of some listed seed enterprises with high financial support efficiency came at the cost of high local carbon dioxide emission and high energy consumption. Internal factors such as operating profit, equity concentration, financial structure, and enterprise size are the key intra-firm factors that affect the efficiency of financial support for listed seed enterprises. Thus, it is suggested that enterprises must pay attention to the environmental performance to reach a win-win situation in reducing and improving the energy consumption and financial performance, respectively. Similarly, the improvement of energy use efficiency through endogenous and external innovation should be prioritized to achieve sustainable economic development.

Does location of the households' matters? Identifying the households' willingness to pay and preference heterogeneity in advancement of vulnerable ecosystem services: An approach of choice experiment.

The present research underlines the need to expand far outside bundling or hierarchical providing strategy that often focuses on a specific habitat or ecosystem and creates a location-based strategy that considers how dependency in other parts of the region with ecosystem functions and processes leads to complements and resources' trade-offs. Thus, for assessment of spatial heterogeneity based on willingness to pay (WTP) for upgrading environmental attributes across Heihe River Basin (HRB), a choice experiment survey was carried out in the entire river basin. The HRB is one of the big inland river in the Northwestern region of China and is selected on basis of its geomorphological and geographical significance. A sum of 1679 individuals were interviewed through choice experiment technique from whole river basin consisting of five main cities and 33 adjoining rural areas. The Random Parameter logit model, Krinsky-Robb technique as well as delta method were applied for the evaluation of spatial heterogeneity and estimation of individual specific WTP, respectively. Spatial heterogeneity is verified among sampled individuals' preferences about upgradation of environmental attributes, such as, observed preferences of individuals' and their varying corresponding WTP amounts for per unit's upgradation in agriculture product quality, greenhouse gases reduction, farmland landscape, and biodiversity, which reflects heterogeneous tastes and preferences of the selected individuals. In addition, the assessed outcomes for identifying the impacts of distance decay through random parameter logit model depicted the vital role of distance influence on respondents' WTP for restoring the degraded environmental attributes, such that among 3 ad hoc distance bands, WTP of those sampled individuals who are in proximity of ≤ 10 km to HRB is more than the rest of the individuals, i.e., individuals living in the range of ≤ 20 km and > 20 km. For instance, WTP for agriculture product quality is 119.147 CNY/year in ≤ 10 km and is higher than the remainders.

Does green finance promote agricultural green total factor productivity? Considering green credit, green investment, green securities, and carbon finance in China.

The Chinese government has proposed a "double carbon" target as a response to climate change and has been incorporated in the vision 2035. Agriculture's green development is intrinsically related to financial support, but conventional financial mechanisms fall short in their efforts to foster economic growth and curb environmental degradation. The current research examines the theoretical effects of green finance on agricultural green total factor productivity in the context of "double carbon." The mediating effect and moderating effect models are used to empirically examine this mechanism based on the panel data (2015-2019) of 30 provinces in China. The estimated results suggest that green finance development can significantly increase agricultural green total factor productivity, with an inverted U effect and an apparent regional heterogeneity. The eastern region has a stronger promotional effect than the central and western regions. The advanced industrial structure plays a mediating effect on the impact of green finance on agricultural green total factor productivity. Rural human capital can effectively mediate the mediating effect of advanced industrial structure on the impact of green finance on agricultural green total factor productivity. The aforementioned results offer fresh perspectives and empirical evidence for China's green finance policy improvement, harmonizing regional green finance development, promotion of industrial structure improvement, and rural human capital optimization.

Implementing a novel deep learning technique for rainfall forecasting via climatic variables: An approach via hierarchical clustering analysis.

Variations in rainfall negatively affect crop productivity and impose severe climatic conditions in developing regions. Studies that focus on climatic variations such as variability in rainfall and temperature are vital, particularly in predominant rainfed areas. Forecasting rainfall is very essential in the agriculture sector due to the dependence of many people, while it is very complex to accurately predict rainfall due to its dynamic nature. This study aims to present a deep forecasting model based on optimized (Gated Recurrent Unit) GRU neural network to predict rainfall in Pakistan based on the 30 years of climate data from 1991 to 2020. The climatic variables were first extracted and then fine-tuned by eliminating outliers and extreme values from the data set for precise forecasting. Data normalization strategies were further utilized to adjust numeric values into a standard scale without distorting divergences or losing useful information. The proposed model achieved high prediction accuracy by maintaining minimal Normalized Mean Absolute Error (NMAE) and Normalized Root Mean Squared Error (NRMSE) compared to state-of-the-art rainfall forecasting models. Climatic variables used in the forecasting were evaluated in terms of correlation and regression analysis. The correlation results showed that temperature has a negative association and air quality variables have a positive association with rainfall in each quarter of the year. The second and third quarters of the year showed a high association with rainfall, whereas the air quality variables showed a lesser or no association with rainfall during the first and second quarters of the year. The results further showed a strong association of climatic variables with rainfall for all months of the year. The minimal loss achieved by the proposed model also demonstrated the feasibility of selected variables in precise forecasting of rainfall regardless of volatile climatic conditions.

An Estimation of the Effect of Green Financial Policies and Constraints on Agriculture Investment: Evidences of Sustainable Development Achievement in Northwest China.

Farming' community actively participating as micro-actors in green finance schemes is critical for regional planning and development. On the basis of the extent to which financial progress and sustainable development are coordinated, in a difference-in-differences approach, this article employed 2350 small investigations to estimate the influence of green-finance strategies on peasants' agriculture investment and developed a mediation effect method. It investigates the role of peasant managerial variability in mediating the influence of financial constraints. The results indicate that the introduction of a financial restriction variable reduces the positive impacts of green-finance regulations on peasants' agricultural investment. Moreover, peasants who participate in non-agricultural management exercises are more inclined to take advantage of green financing regulations and are affected via financial restrictions in mediate means. The building of a green-finance sector in remote regions should accomplish unique positioning and rapid growth.

Exploring the spatiotemporal heterogeneity and influencing factors of agricultural carbon footprint and carbon footprint intensity: Embodying carbon sink effect.

Due to the combined effects of carbon emission and carbon sink, agriculture is acknowledged as an essential contributor to achieve the Chinese government's carbon neutrality goal of 2060, and carbon footprint (CF) and carbon footprint intensity are substantial indicators to reveal the carbon emission level. For these reasons, the Theil index technique and extended STIRPAT model were employed to evaluate their spatiotemporal heterogeneity and influencing factors using panel data from 31 provinces for the period 1997-2019. The findings revealed that the CF showed an increasing trend with an annual growth rate of 24.6 %. The carbon footprint intensity (CFI) indicated an evident spatiotemporal heterogeneity and transferred over time, with an average growth rate of 19.82 %. The CFI Theil index and its contribution rate both confirmed that intra-regional difference is the main source of the overall difference, among which, the CFI Theil index displayed the distribution feature of "western (11.50 %) > central (11.12 %) > eastern (10.56 %) > northeast (6.61 %). The contribution rate of CFI illustrated the spatial pattern of "eastern (33.74 %) > central (21.07 %) > western (19.87 %) > northeast (5.24 %). Furthermore, the influencing effects of GDP per capita, planting structure, population density and urbanization level on CF and CFI also demonstrate evident spatiotemporal heterogeneity.

Quantifying the spatial preferences heterogeneity to upgrade the endemic ecological attributes for a cleaner environment: Evaluating the feasibility of benefit transfer among sub-basins of an inland river.

The role of wetlands in the conservation of global biodiversity is very significant due to the provision of various benefits to households. The assessment of these benefits and general public perception in terms of their willingness to pay for restoring the degraded ecosystem services is also heterogeneous because of their spatial preferences. This research employed a choice experiment technique to evaluate willingness to pay of the households to improve the current degraded ecosystem services and assessed benefit transfer among upper, middle and lower sub-basins of Heihe River. The outcomes of the study were estimated through two models, i.e. the conditional logit and random parameter logit models. The heterogeneous willingness to pay amounts for ecosystem services confirm the variations in taste and preferences of the respondents across all sub-basins. Considering spatial heterogeneity of the preferences, the random parameter logit model resulted in smaller transfer errors than the conditional logit model. The values of transfer errors estimated by the random parameter logit model from the lower to the upper sub-basin, from the upper to the middle sub-basin and from the middle to the lower sub-basin were 17.76%, 80.65% and 33.92%, respectively, and the reason for these smaller values is the consideration of taste and preferences of the respondents. On the other hand, the estimated values of transfer errors for the same settings in the conditional logit model were 21.93%, 109.31% and 43.08%, respectively. The transfer error values thus validate the transfer of benefits across all the sub-basins of the river. Therefore, the current findings are helpful in proper management of ecosystem services and permit for benefit transfer from one sub-basin to another sub-basin of Heihe River.

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

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

The asymmetric effects of crops productivity, agricultural land utilization, and fertilizer consumption on carbon emissions: revisiting the carbonization-agricultural activity nexus in Nepal.

Agriculture plays an integral part in facilitating socioeconomic development in Nepal. However, it is also associated with environmental concerns which need to be controlled for the sake of ensuring environmental and agricultural sustainability in tandem. Against this backdrop, this current study aims to check whether shocks to the levels of agricultural productivity, land utilization for crop production, and fertilizer consumption influence the carbon dioxide emission figures of Nepal over the 1965-2018 period. The long-run associations between these variables are confirmed from the cointegration analysis. Besides, the outcomes from the asymmetric non-linear autoregressive distributed lag regression analysis show that crop productivity does not influence the emission levels in Nepal. However, a decline in the land area used for crop production purposes is evidenced to trigger higher emissions of carbon dioxide both in the short- and long run. On the other hand, higher fertilizer consumption is found to boost the short- and long-run carbon dioxide emission levels in Nepal. Accordingly, considering the objective of reducing agriculture-based emissions, this study recommends the Nepalese government to adopt policies that can enhance the productivity of low energy-intensive crop production, stimulate green agriculture and non-agriculture activities, and minimize the use of chemical fertilizers in arable lands.

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

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

Identifying impact of international trade and renewable energy consumption on environmental quality improvement and their role in global warming.

There is a lack of proper research that highlights the impact of institutional quality (IQ) and renewable energy consumption (REC) on the carbon emission (CE). The significance of IQ and REC in the achievement of zero CE is highlighted in this research. The current research reports the effects of these important factors on the consumption-based carbon emissions in the G-7 countries from 1995 to 2018. Based on the outcome of the cointegration test, the long-run connection is recognized between IQ, REC, GDP, exports, imports, and consumption-based CE. The findings also validated that there exist significant decrease and increase in the CE in both the short and long run; for instance, IQ, REC, and exports decrease the CE, while imports and GDP increase the CE. The estimates of causality test showed that policies aimed at improving IQ, REC, GDP, exports, and imports have a significant impact on the CE. Consequently, based on these results, policymakers in the G-7 must prioritize IQ and REC to enhance environmental quality and attain carbon neutrality.

The spatiotemporal dynamic and spatial spillover effect of agricultural green technological progress in China.

The progress of agricultural green technology is an important means and fundamental way to achieve high-quality development of agriculture. The current study takes the panel data of 31 provinces in China from 1998 to 2018 and uses the Epsilon Based Measure-Global Malmquist-Luenberger (EBM-GML) model to measure China's agricultural green technological progress (AGTP) and discusses its dynamic evolution characteristics in the spatiotemporal dimensions. Finally, we analyze the spatial spillover effects of AGTP by the spatial Dubin model. The results show that China's AGTP showed a trend of first rising and then falling, and the average value is 1.0525. AGTP has obvious regional unbalanced development, and the regional differences are expanding. It shows that AGTP between adjacent areas is closely linked. The Moran's I index shows that AGTP has a significant positive spatial correlation. The local Moran's I index shows that AGTP is concentrated in Northwest, Northeast, and North China, and green technological is degraded in East and South China. From the spatial spillover effects of AGTP, the level of agricultural economic development, real GDP per capita, and urbanization have significantly promoted AGTP in the local and neighboring areas, while the agricultural internal structure and the level of labor inhibit AGTP in the local and neighboring areas. In addition, the administrative environmental policy (ENVP) and the economic environmental policy (ECOP) have negative impacts in neighboring areas, while the policy has negative spillover effects and positive spillover effects in the local area, respectively. Therefore, we should adhere to the concept of green development, pay attention to the regional exchange of green technology, concentrate policies on low-low concentration areas, and increase the follow-up tracking and supervision mechanism of the policy design and implementation process.

Role of institutional quality and renewable energy consumption in achieving carbon neutrality: Case study of G-7 economies.

This research emphasized the significance of institutional quality and renewable energy consumption in achieving the desired objective of zero carbon emissions. Following the COP21 (Paris Conference), many countries have fixed their national level objectives for achieving the carbon neutrality and to tackle the problem of global warming. Many researchers have focused their efforts on the aspects that contribute to environmental degradation. Meanwhile, there is a scarcity of appropriate research that highlights the environmental implications of institutional quality and renewable energy consumption. Therefore, the current study examines the influence of these significant determinants on CCO2 emissions in the G-7 countries from 1990 to 2018. The long run relationship between institutional quality, renewable energy consumption, GDP, exports, imports, and CCO2 emissions is approved, based on the assessed results of cointegration test. Besides that, the estimated results have endorsed a considerable decrease and increase in carbon emission both in the short and long run, i.e., institutional quality, renewable energy consumption, and exports reduce emissions, while imports and GDP raise emissions. The Dumitrescu and Hurlin causality test results show that policies aiming at improving institutional quality, renewable energy consumption, GDP, exports, and imports have a significant effect on CO2 emissions. As a result of these findings which recommends that G-7 countries' policymakers should emphasize institutional quality and renewable energy consumption in order to improve environmental quality by reducing carbon emissions and to achieve carbon neutrality.

An insight into the drag effect of water, land, and energy on economic growth across space and time: the application of improved Solow growth model.

Economies that depend on natural resources can experience a resource drag effect when economic growth is limited by constraints on the availability of those resources. Therefore, this study uses panel data and the improved Solow growth model to explore the resource drag effect on China's regional economic growth from 1987 to 2017 and makes innovative contributions to address these four gaps in the previous literature: the resources gap, the consistent measurement gap, the regional gap, and the temporal gap. The empirical results indicate that the resource drag effect reduced China's overall annual economic growth by 0.58% during the study period, with reductions of 1.07%, 0.29%, 0.79%, and 0.46% in the Eastern, Western, Central, and Northeastern regions, respectively. In the meantime, the resources drag effect changed in individual regions and across regions. The results on energy drag are most notable. Policies such as "West-to-East Electricity Transmission" and "West-to-East Gas Transmission" promoted economic growth of the Eastern and Western Region, facilitating continued growth in both regions and attracted the return of labor to the Western region. The results indicate that the policies such as west-to-east energy transfer for helping to even out the economic growth conditions in different regions. Labor force mobility has also been important to alleviate resource dependence of agricultural production in Central regain, while other regions have managed to continually grow through improvements in inefficiency. Also, growth in some regions/provinces continues to depend upon increases in water, land, and energy availability and export. This will become increasingly problematic as the social prices of these inputs rise to account for environmental damage. Therefore, the government should adjust the industrial structure of each region to optimize use of resource endowments, alleviate dependence on natural resources, and achieve sustainable economic development.

An impact of climate change and groundwater salinity on shadow price of water, farmers' revenue, and socioeconomic and environmental indicators in district Kohat-Pakistan.

Globally, agricultural productivity is adversely impacted due to climatic changes as the temperatures rises and precipitation decreases, and especially in Pakistan, which ultimately enhanced groundwater salinity and harmed water quality in the country. However, the impacts of groundwater salinity and climate change on farmers' revenue have not been fully understood in Pakistan. Therefore, the focus of current research is the assessment of shadow price of water, farmers' revenue, and socioeconomic and environmental indicators affected by variations in groundwater salinity, precipitation, and temperature. The estimation of crop yield sensitivity to groundwater salinity, precipitation, and temperature and their prediction for 2030, 2040, and 2050 time periods was accomplished through the technique of General Maximum Entropy and Response-Yield function. Moreover, the assessment of groundwater quality and climate variable impacts on socioeconomic and environmental indicators was obtained through Target Motad-PMP model. In the end, the most suitable climate change scenario in the study area was established by applying a multi-criteria decision-making method. The results revealed that groundwater salinity and temperature expressed a significantly increasing trend with the Z values of 5.82 and 2.15, respectively. While the precipitation depicted a significantly decreasing trend (Z value = -3.37). The negative impact of climatic changes and groundwater salinity was revealed for revenue risk and shadow prices of water. The most negative impact on income risk and shadow prices is during 2050 horizon with a decrease by 11.4 and 19.4% respectively. The environmental index is the most important with a priority of 43.4% compared to the socio-economic indicators. The sub-index water use is also significant in the study area with a priority of 28.1%. A2 is the most appropriate climate scenario conferring to the TOPSIS ranking method. Therefore, the A2 scenario should be taken into account for the policy of adaptation to the climate change wonder in district Kohat.