Administrative Region Of China Research Articles

Factors Influencing the Intention of Chinese Adults to Recommend COVID-19 Vaccination for Specific or Non-Specific Groups

The widespread availability of vaccines has profound implications for sustainable public health. Positive recommendation on vaccination is one of the most effective ways to increase the willingness to vaccinate against COVID-19. This study aims to investigate the factors influencing the intentions to recommend COVID-19 vaccination for specific groups (IRCVSG) and the intentions to recommend COVID-19 vaccination for non-specific groups (IRCVNSG) in China and explore the mediating role of vaccine hesitancy and perception of vaccination information. This study conducted a cross-sectional anonymous online survey of adults (N = 903) in 28 provincial-level administrative regions in China in May 2022. The prevalence of IRCVSG and IRCVNSG was 60.5% and 93.0%, respectively. Health information literacy has a significant direct and indirect impact on IRCVSG through safety hesitancy and the perceived adequacy and usefulness of vaccination information. The relationship between health information literacy and IRCVNSG is entirely mediated via hesitation about the effectiveness and perceived usefulness of vaccination information. Special attention should be paid to the safety hesitation of COVID-19 vaccination for specific groups. This study tests these effects from both theoretical and practical perspectives, helping to address barriers to promoting the vaccination of specific groups for COVID-19 in clinical practice, improving health and sustainability.

The Impact of the Digital Economy on Population Dividends in China: Based on the Dual Perspective of Quantity and Quality

The demographic dividend plays an important role in promoting sustainable development in China. Here, we ask the question of how to use the digital economy to coordinate the “one body and two sides” of the demographic dividend. This study empirically examines the impact of digital economic development on the demographic dividend in a multidimensional way based on the panel data from 30 provincial-level administrative regions in China from 2011 to 2020. The study results show that (1) the digital economy significantly promotes the demographic quality dividend but exhibits a suppressive effect on the demographic quantity dividend; (2) the digital economy can indirectly influence the demographic quality and quantity dividends through urbanization; (3) when examining the threshold effects, the study uncovers noteworthy dynamics, whereby the urbanization levels serve as significant thresholds, showcasing “diminishing marginal effects” in the digital economy’s influence on both population quantity and quality dividends; (4) digital economic development has a positive spillover effect on the demographic quantity dividend in adjacent areas. By clarifying these dynamics, the research results provide valuable insights into China’s sustainable use of the digital economy to create a demographic dividend.

Financial deepening does cause income growth of rural residents: Insights from the relationship between government and market in China

This paper examines the impacts of government-market relation (measured by the degree of marketization) on the link between financial deepening and income growth of rural residents. Using macro panel data covering a period from 2010 to 2021 across 31 provincial administrative regions in China, we create an interaction variable that captures the interplay between financial deepening and the degree of marketization. Employing a fixed effects model and a dynamic panel regression model, we derive three robust findings from the regression analysis: (1) the higher the degree of marketization, the stronger the promotion effect of financial deepening on the growth of rural residents' income, with a one-year lag effect; (2) the higher the degree of marketization, the stronger the promotion effect of financial deepening on the wage income of rural residents, with a one-year lag effect; (3) the higher marketization fails to create the promotion effects of financial deepening on the net business income and net property income of rural residents. These results suggest that financial deepening aimed at raising rural residents' incomes in provinces, where China's marketization has slowed down, stagnated, or retrogressed, will have difficulty reaching its goals, and that some of the financial and economic problems affecting the growth of rural residents' business and property incomes remain unresolved.

Predicting influenza-like illness trends based on sentinel surveillance data in China from 2011 to 2019: A modelling and comparative study1

BackgroundInfluenza is an acute respiratory infectious disease with a significant global disease burden. Additionally, the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions (NPIs) have introduced uncertainty to the spread of influenza. However, comparative studies on the performance of innovative models and approaches used for influenza prediction are limited. Therefore, this study aimed to predict the trend of influenza-like illness (ILI) in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive performance. MethodsThe generalized additive model (GAM), deep learning hybrid model based on Gate Recurrent Unit (GRU), and autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA—GARCH) model were established to predict the trends of ILI 1-, 2-, 3-, and 4-week-ahead in Beijing, Tianjin, Shanxi, Hubei, Chongqing, Guangdong, Hainan, and the Hong Kong Special Administrative Region in China, based on sentinel surveillance data from 2011 to 2019. Three relevant metrics, namely, Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R squared, were calculated to evaluate and compare the goodness of fit and robustness of the three models. ResultsConsidering the MAPE, RMSE, and R squared values, the ARMA—GARCH model performed best, while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in China. Additionally, the models’ predictive performance declined as the weeks ahead increased. Furthermore, blocked cross-validation indicated that all models were robust to changes in data and had low risks of overfitting. ConclusionsOur study suggested that the ARMA—GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid model. Therefore, in the future, the ARMA—GARCH model may be used to predict ILI trends in public health practice across diverse climatic zones, thereby contributing to influenza control and prevention efforts.

How policy promotes the integration of culture and tourism? A fuzzy-set qualitative comparative analysis based on the Policy Instrument Theory.

Crafting pertinent policies to facilitate the high-level integration of culture and tourism has now become a vital agenda within the current discourse in China. However, relatively little is known about the actual implementation of various policies to achieve a high-level integration, especially how combinations of policy instruments are deployed in the process of realization. Based on the Policy Instrument Theory, this study uses fuzzy-set qualitative comparative analysis on a sample of 31 provincial administrative regions in China to investigate the influence of typical policy instruments on the integration level of tourism and culture. The results show that each single policy tool is not necessary for high-level integration of culture and tourism. On the contrary, only through an organic combination of different policy tools can affect the integration level. This study also summarizes five policy instrument configurations, which can be grouped into four driving modes of culture-tourism integration: the environment-driven supply-demand coordination mode, supply-driven demand-environment coordination mode, supply-driven mode, and supply-driven environment coordination mode. This study considerably provides critical theoretical and practical insights into the integration of culture and tourism from the perspective of governmental policies.

The Impact of the Rural Digital Economy on Agricultural Green Development and Its Mechanism: Empirical Evidence from China

Agricultural green development represents an environmentally friendly and resource-efficient agricultural model, and it is a key way to achieve sustainable agricultural development. With the rapid rise of the digital economy, its influence is gradually spreading from urban to rural areas, and it has played a significant and far-reaching role in promoting the green transformation of agriculture. This paper employs the entropy weight method to measure the level of digital economy and agricultural green development in rural areas in 30 provincial administrative regions in China from 2012 to 2021 and analyzes the relationship between the two and the mechanisms behind it. The research results show that (1) the rural digital economy significantly promotes agricultural green development. (2) With the enhancement of agricultural green development, the impact of the rural digital economy on it initially increases and then declines. (3) The rural digital economy fosters agricultural green development by advancing agricultural technology, easing credit constraints, and promoting agricultural industry agglomeration. (4) Environmental regulation intensifies the positive influence of the rural digital economy on agricultural green development. This research significantly enhances our understanding of the mechanism by which the rural digital economy facilitates agricultural green development. It offers empirical evidence and recommendations for the government to formulate and implement effective policies to advance agricultural green transformation in the context of digital economy trends.

The Impact of the Integration of the Culture Industry and Tourism on Regional Green Development: Empirical Evidence from China

The concept of “green development” has garnered increasing recognition and prominence in recent years, attracting the attention of various countries and regions. How to achieve green development has become a pressing issue for the government. The integration of the culture industry and tourism has had a profound impact on regional green development. This paper employs the entropy method to assess the level of regional green development, and it employs the coupling coordination model to evaluate the degree of integration of the culture industry and tourism across 30 provincial administrative regions in China from 2011 to 2021. Additionally, an econometric model is constructed to empirically examine the impact of the integration of the culture industry and tourism on regional green development, as well as its underlying mechanism. The findings of this study indicate the following: (1) The integration of the culture industry and tourism significantly enhances regional green development. (2) With the improvement in the regional green development level, the integration of the culture industry and tourism is playing a gradually stronger role in promoting regional green development. (3) Further analysis of the mechanism reveals that the integration of the culture industry and tourism enhances regional green development by facilitating the upgrading of the tourism industrial structure. (4) Environmental regulation policies reinforce the role of the integration of the culture industry and tourism in promoting regional green development. These research findings contribute to enhancing our understanding of the impact of the integration of the culture industry and tourism on regional green development, providing empirical evidence and policy recommendations for the government to facilitate the integration of the culture industry and tourism and further promote green development.

Access to innovative anticancer medicines in China: a national survey on availability, price and affordability

ObjectivesThis study aimed to investigate the availability, price, and affordability of nationally negotiated innovative anticancer medicines in China.DesignRetrospective observational study based on data from a nationwide medical database.Data sources/settingQuarterly data...

Research on the Evaluation Method and Application of Provincial Differentiated Carbon Peaking in China

Promoting regions with favorable conditions to take the lead in reaching a carbon peak is an inevitable step towards achieving the dual carbon goals under the "nationwide coordinated action" plan. Considering the differences among Chinese provinces, this study measured the peaking pressure of each province based on the spatial distribution of carbon emissions. We then constructed a provincial peaking capacity evaluation system based on five dimensions, namely, peaking pressure, emission reduction status, economic development, policy support, and resource endowment, to comprehensively evaluate the carbon peaking capacity of 30 provincial administrative regions in China, excluding Hong Kong, Macau, Taiwan, and Tibet, using the entropy value method to determine the index weights. The 30 provinces were divided into five peaking tiers according to the evaluation results. The results showed that:① 18 regions, such as Hainan and Beijing, displayed a surplus in carbon emission space; eight regions, including Hebei and Shandong, showed a deficit in carbon emission space; and the carbon emission spaces allocated to Zhejiang, Anhui, Henan, and Hubei were comparable to their respective actual emissions. ② Developed regions generally had a higher carbon peaking capacity than that of less developed regions, with Beijing and Shanghai showing outstanding carbon peaking capacity, whereas Jiangxi and Guizhou had more room to improve their capacity. Finally, differentiated peaking targets and priority actions were proposed according to the provinces' different peaking tiers and local conditions.

Flavor analysis and region prediction of Chinese dishes based on food pairing

There are thirty-four provincial administrative regions in China, and each region possesses its own distinct food culture. However, existing studies on flavor-based research often treat Chinese dishes from various regions as a single entity, resulting in biases when examining regional distinctions and the variety of recipes. To explore the potential correlation between food ingredients and regions, this paper performs flavor analysis and region prediction for Chinese typical dishes. A new dataset including 1534 recipes combined with a compound-ingredient list to construct a compound-ingredient-recipe network. The results indicate that in terms of food ingredient selection, Hainan shows the highest degree of flavor compound sharing, while Shanghai does the lowest. While all the regions studied tend to exhibit negative food pairing according to the food pairing hypothesis, a region with a high degree of flavor sharing is more able to become prominent representation of the corresponding flavor branch through the analysis of representative ingredients. Across the 14 regions studied, the frequency of ingredient usage within a region plays a crucial role in maintaining its distinct flavor profile. Concerning the individual contributions of ingredients to flavor compound sharing, the positive contributions are linked to rice and natural spices, including Welsh onion, ginger, and garlic, while the negative contributions are primarily associated with flour, pork, and traditional Chinese condiments such as soy sauce, cooking wine, and grain vinegar. Multiple deep learning experiments demonstrate the effectiveness of combining ingredient and flavor information to region prediction for recipes.

Does the South-to-North Water Diversion Project promote the growth of enterprises above designated size in the water-receiving areas?-Evidence from 31 provincial-level administrative regions in China.

The South-to-North Water Diversion Project (SNWDP) is believed to drive the next phase of sustainable productivity growth, meeting growing water demand, so as to address increasing environmental sustainability challenges. The Middle Route of SNWDP is regarded as an extremely large long-distance inter-basin water diversion project, which has benefited Henan, Hebei, Tianjin and Beijing since 2014 with great sustainable changes to the cities, groundwater, ecological environment, industrial structure and social development of the beneficiary areas. Taking the number of industrial enterprises above designated size (IEDS) in the water-receiving areas as the research object, this paper takes the year of policy implementation 2014 as the basic time point, evaluating the change of the number of IEDS in the beneficiary areas of the Middle Route of SNWDP through difference-in-difference model. The results show that: (1) The Middle Route of SNWDP promotes the additional growth of the number of IEDS in the beneficiary areas. (2) When the Middle Route of SNWDP promotes the growth of the number of IEDS in beneficiary areas, there is no regional difference for regions with different development levels. (3) The reasons why the Middle Route of SNWDP contributes to the additional growth of the number of IEDS are composed of promoting mixed ownership reform of enterprises in beneficiary areas, increasing water supply and increasing population. However, the Middle Route of SNWDP has not had a significant impact on the traditional total factor productivity or the components of production factors, technology and capital. From the final outcome, the South-to-North Water Diversion Project has played a facilitating role in the sustainable development of large-scale enterprises in the water-receiving areas.

Measuring water-used and production efficiency in China using the super-efficient directional distance function

ABSTRACT This study employs the super-efficiency directional distance function (SDDF) to assess the productivity of each administrative region in China over the period 2013–2017. The focus is on exploring variations in gross domestic production and efficiency related to waste gas and wastewater discharge across regions. The inputs include labor, capitalization, energy usage, and total water consumption, with domestic gross production as the output, and total wastewater and exhaust gas discharges as unintended outputs. The findings highlight Beijing, Tianjin, Hainan, Qinghai, Guangdong, Shanghai, Jiangsu, and Shandong as the most efficient regions, while Zhejiang, Ningxia, Hunan, and others exhibit lower performance. Notably, Guangxi ranks lowest (0.631). Unlike traditional direct distance function models, the SDDF model provides a more accurate estimation of the production efficiency of all 30 administrative regions, and addresses the limitation of generating the same efficiency values of 1 simultaneously for multiple regions. The study emphasizes the need for inefficient regions to reduce water consumption and emissions and enhance productivity, offering valuable insights for policymakers in formulating environmental and production policies.

中国野生报春花属物种多样性与地理分布数据集

Catalogue of Life serves as the background baseline information for the biota of a given natural geographic region or administrative unit. The timely updating of its dynamics is an important basis for the research, management and monitoring of biodiversity conservation. The integration and sharing of biodiversity data can provide a scientific basis for the development of related studies. Primula L. is a typical genus characterized by a broad distribution and a high degree of endemism, with diverse habitats and a complex taxon structure. An in-depth analysis of its geographical distribution pattern and characteristics can provide a foundational basis for the conservation and use of germplasm resources. With the aim of offering a reference for germplasm resource surveys and application research on seed introduction, and based on a large amount of basic data and literature, this paper adopts the taxonomic system used by Flora of China to update the list of wild species of the genus Primula L. distributed in the terrestrial area of China. In the paper, we counted their species diversity, composition and other information, and established a dataset of the diversity and geographical distributions of wild Primula L. in China. This genus predominantly thrives in the temperate and subtropical zones of the Northern Hemisphere. Its diversity center and modern distribution center are also widely distributed in the eastern Himalayas and Hengduan Mountains in broad sense, Tianshan Mountain Area, Qinba Mountain area, Wuling Mountains Area, Qinghai Tibet Plateau, Yunnan Guizhou Plateau and other high-altitude areas. Additionally, it extends to middle and lower altitude areas with prominent karst and Danxia landscapes. As of October 2022, a total of 397 species (28 varieties and 26 subspecies) of the genus Primula L. have been recorded in China, including 296 endemic species (including subspecies classification units, the same below) in China. In addition to the five administrative regions of Jiangsu, Hainan, Shanghai, Hong Kong and Macao, the distribution of wild primroses has been recorded in various provincial administrative regions in China, with Sichuan Province boasting the highest species richness and endemism at the provincial scale (177 species), followed by Yunnan Province (176 species) and the Tibet Autonomous Region (171 species). At the county scale, Shangri-La City ranks first in species richness with 62 species.

Spatiotemporal heterogeneities in the impact of the digital economy on carbon emission transfers in China

This study investigates the spatiotemporal impacts of digital economic development on interprovincial carbon emissions transfer in China. Applying a proposed mixed geographically and temporally weighted spatial interaction panel regression (MGTWIPR) model, the study analyzes the annual carbon emissions data of 31 provincial administrative regions in China from 2015 to 2017. The results show that China's interprovincial carbon emissions transfers have apparent asymmetrical and imbalanced characteristics. The most significant transfers from the Yangtze River Delta (YRD) and Pearl River Delta (PRD) regions to north China and its surrounding areas follow a general pattern of carbon emissions transfer from developed to less developed regions. Improving digital economic development in origin regions promotes the exportation of carbon emissions to other areas. Conversely, enhancing digital economic development in destination regions would inhibit the import of carbon emissions to those regions. Additionally, from a spatial perspective, the impact of the origin region's digital economic development on carbon transfer is maximized in western China. Temporally, the influence of the origin region's digital economic development on carbon emissions transfers increases over time. This study integrates research regarding the digital economy and carbon emissions transfer, providing a novel perspective for digital economic studies. It also introduces a new research method for investigating the spatiotemporal heterogeneity of panel data. The results suggest the development of targeted strategies for regions with net carbon emissions exports and imports, emphasizing the role of digital technology in curbing carbon emissions to meet China's dual carbon goals.

Economy and carbon dioxide emissions effects of energy structures in China: Evidence based on a novel AHP-SBMDEA model

With the completion of the poverty alleviation task in China, the Chinese economy has made great breakthroughs. Meanwhile, the implementation of Chinese "dual carbon" goal has entered an important window period of both strategic opportunities and difficulties. The emission of carbon dioxide (CO2) leads to the greenhouse effect, which affects the sustainable development of society. Therefore, this paper proposes an improved analytic hierarchy process (AHP) with Slacks-based measure integrating data envelopment analysis (SBMDEA) (AHP-SBMDEA) based efficiency evaluation model for different provincial-level administrative regions in China, which is used to analyze and optimize the Chinese energy structure of different provincial-level administrative regions. With coal, gasoline, petroleum, fuel oil, diesel, crude oil and natural gas as inputs, the per capita Gross Domestic Product (GDP) as the desired output and the CO2 as the undesired output, after linear weighted calculation of the results of the AHP-SP model and the SBMDEA model, the energy efficiency evaluation model of different provincial administrative regions in China is established. The results show that there is a large difference in efficiency between Chinese northern and southern regions, and the overall efficiency of southern regions is higher than that of northern regions. In addition, it is easier to find out the optimal energy efficiency benchmark, which can provide suggestions and help to guide the energy consumption input structure improvement of other inefficient provincial-level administrative regions. Furthermore, the energy structure can also be adjusted through the redundant variable calculated by the SBMDEA model. For example, Sichuan Province can reduce the consumption of the coal, the gasoline, the diesel, the fuel oil, the crude oil and the natural gas by 5269.95 ×10000 tons (WT), 513.01 WT, 59.05 WT, 34.37 WT, 4,73.33 WT and 130.37 billion cubic meters (BCM), respectively. Its CO2 emissions can be reduced by 1925.4 WT, and raise the efficiency of energy utilization in the province.

Study on the effect of digital economy development on carbon emissions: evidence from 30 provinces in China.

Currently, China is moving towards the era of the digital economy, which is gradually becoming a new engine of high-quality development. In the "double carbon" strategy context, the digital economy is characterized by low carbon emissions and high permeability, making it essential for carbon emission reduction. There needs to be more empirical research on the digital economy and carbon emissions. Given this, this study empirically examines the impact of digital economy development on carbon emissions intensity and its mechanisms in a multidimensional way based on the panel data of 30 provincial-level administrative regions in China from 2011 to 2019, utilizing a fixed-effects model, a mediated-effects model, a spatial Durbin model, and other methods. The study results show that (1) the digital economy can significantly reduce carbon emissions intensity. (2) The digital economy can indirectly affect the intensity of carbon emissions through industrial structure, energy structure, and environmental regulation. (3) The development of the local digital economy has a positive spillover effect on the carbon emissions intensity of neighbouring places. However, the overall effect is negative. This paper reveals some new features of the digital economy and carbon emissions intensity, which provides a reference for advancing the country's construction and realizing China's "double carbon" goal.

Economy and carbon emissions optimization of different provinces or regions in China using an improved temporal attention mechanism based on gate recurrent unit

With the implementation of 14th Five-Year Plan in China and the completion of the poverty alleviation task, the economy in China has made great progress. However, the carbon dioxide (CO2) emission has also increased, which will lead to the greenhouse effect for threatening economic development and affecting people's lives. Global warming has become the greatest challenge to contemporary economic and social development, and the transition to low-carbon economy has become a general trend of world economic development. Meanwhile, the traditional neural network method for predicting the economy and CO2 emissions is not accurate and objective because of the unbalanced labeling of training data and the inability to capture the relationship between time series data. Therefore, an economy and CO2 emission prediction model based on temporal attention Gate Recurrent Unit (GRU) (TA-GRU) with loss function Balanced Mean Square Error (BMSE) (BTA-GRU) mechanism is proposed to analyze and optimize the energy structure of 27 provincial-level administrative regions in China. Due to the unbalanced data labels in the different energy structure of different regions, the improved loss function BMSE is used to solve the problem of unbalanced labels from the perspective of statistics. Then, with coal, gasoline, petroleum, coke, fuel oil, diesel, natural gas and crude oil as inputs, the per capita Gross Domestic Product (GDP) as the desired output and the CO2 as the undesired output, the GRU can better capture time step dependencies in time series, and the attention mechanism can assign different weights to each time step, so that the proposed method can better learn data features and improve the accuracy of the an economic and CO2 emission prediction model. Finally, the CO2 emission and economic prediction model of different provincial-level administrative regions in China is established based on this proposed method. The experimental results show that compared with other mainstream time series data prediction models, this proposed model has better stability and practicability, and its mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) reach 3.43, 3.28% and 2449.57, respectively. Furthermore, according to the actual situation of efficient provincial administrative regions, the energy structure of inefficient provincial administrative regions can be adjusted and optimized. Such as the energy structure of Hebei Province can be adjusted according to the energy structure of Beijing, it is estimated that the CO2 emissions of Hebei will be reduced by 82,972.43 ×10000 tons (WT) and its per capita GDP will be increased by 115,856 Yuan. Moreover, Chinese provincial governments should vigorously promote the use of clean energy, accelerate the establishment of a sound green low-carbon circular development economic system, and help achieve the goal of carbon neutrality.

Green total factor productivity growth and its driving forces in China’s fisheries sector

The rapid development of China’s fisheries sector has brought about significant environmental problems, which are detrimental to the sustainable development of the sector. Balancing environmental protection while promoting fisheries development has become an urgent issue in China. Based on data from 30 provincial-level administrative regions in China from 2004 to 2019, this study utilizes the Epsilon-based Measure (EBM) model considering undesirable outputs and the global Malmquist-Luenberger (GML) index to measure the green total factor productivity (GTFP) growth in China’s fisheries sector. Furthermore, it explores the spatiotemporal evolution and driving forces of fisheries GTFP growth using spatial Durbin model (SDM). The results indicate that ignoring the resource and environmental costs in fisheries production would overestimate the growth of total factor productivity (TFP) by 1.3%. The growth of fishery output primarily comes from the increase in input factors, exhibiting extensive characteristics that have been gradually diminishing over time. During the sample period, the provinces with the fastest growth in GTFP shifted from being mainly concentrated in the central and western regions to the eastern region. The local driving forces behind the improvement of fisheries GTFP include internet penetration rate, transportation convenience, education level of rural residents. The driving forces from economically similar provinces include the positive spatial interplay between provinces, fishery disaster rate, fisherman training, fishery trade openness, and urbanization rate. Overall, these finds offer a novel approach to reexamine the growth of China’s fisheries and provide valuable insights for the future fisheries development.

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.

Spatial distribution and influencing factors of China's household biogas: evidence from provincial-level data.

To address the adjustment of the Chinese agricultural industry and to better promote the development of Chinese household biogas, this article summarizes and analyzes the spatial distribution characteristics and influencing factors of the type and number of biogas digesters, biogas production, biogas fermentation materials, and methods of fermentation residue utilization and ecological agriculture with household biogas by compiling a dataset covering 31 provincial administrative regions in China. The results show that hydraulic biogas digesters are distributed mainly in northwestern and northeastern China; in addition, continuously stirred biogas digesters and bottom-discharging biogas digesters are distributed mainly in southern and northern China, respectively. Because of temperature and population, the Sichuan and Henan Provinces have the highest number of biogas digesters and biogas production. The type of biogas fermentation materials depends on the local raw materials. Biogas slurry and residue are widely used as fertilizers; furthermore, biogas slurry is used for seed soaking in northeastern and southern China, and biogas residue is used as feed in central southern and northern China. The "Three-in-one" and "Four-in-one" biogas ecological models are used mostly in southern and northern China, respectively, and both are mainly affected by temperature. Finally, we propose various problems and countermeasures to enhance the development of the household biogas industry in China. Our findings are critical for China's policymakers to adopt effective measures for promoting the development of cleaner energy and the layout of the agricultural industry.