SDM Model Research Articles

Assessing eco-geographic influences on COVID-19 transmission: a global analysis.

COVID-19 has been massively transmitted for almost 3years, and its multiple variants have caused serious health problems and an economic crisis. Our goal was to identify the influencing factors that reduce the threshold of disease transmission and to analyze the epidemiological patterns of COVID-19. This study served as an early assessment of the epidemiological characteristics of COVID-19 using the MaxEnt species distribution algorithm using the maximum entropy model. The transmission of COVID-19 was evaluated based on human factors and environmental variables, including climate, terrain and vegetation, along with COVID-19 daily confirmed case location data. The results of the SDM model indicate that population density was the major factor influencing the spread of COVID-19. Altitude, land cover and climatic factor showed low impact. We identified a set of practical, high-resolution, multi-factor-based maximum entropy ecological niche risk prediction systems to assess the transmission risk of the COVID-19 epidemic globally. This study provided a comprehensive analysis of various factors influencing the transmission of COVID-19, incorporating both human and environmental variables. These findings emphasize the role of different types of influencing variables in disease transmission, which could have implications for global health regulations and preparedness strategies for future outbreaks.

Can green finance reduce carbon emission? A theoretical analysis and empirical evidence from China.

As an important way for China to achieve its dual-carbon goal, green finance has become the foundation for promoting high-quality economic development in China. In order to clarify the mechanism of green finance on carbon emissions, this paper puts green finance into the economic model and deduces the relationship between green finance and carbon emission reduction. This paper is based on the panel data of 30 provinces in China (excluding Tibet, Hong Kong, Macao, and Taiwan) from 2008 to 2019, using the individual fixed effect model, dynamical model, mediator model, and SDM model to study the impact of green finance on carbon emissions and its impact path of upgrading of the industrial structure and the development of science and technology based on the measurement of the green finance development index of each province by the entropy method. The findings show that the development of green finance can reduce carbon emission significantly, which can be sustained until at least the third phase and generates spatial spillover effects; regional heterogeneity analysis finds that the development of green finance shows geographical discrepancies: compared with the eastern and western regions, the development of green finance in central region can reduce carbon emissions more significantly; not only can the development of green finance directly reduce carbon emission, but also through the upgrading of industrial structure and technological innovation. The research not only provides a new perspective and supplementary empirical evidence for understanding the carbon emission reduction effect of green finance, but also offers some useful references for green finance to contribute to carbon emission reduction.

Can Green Credit Improve the Innovation of Enterprise Green Technology: Evidence from 271 Cities in China

With the promotion of the “carbon neutrality” and “carbon peak” initiatives, green credit plays an important role in helping enterprises to change their high-pollution, high-energy-consumption production methods and establishing a sound green, low-carbon, and circular economic system. This study used spatial correlation analysis and a fixed effects SDM model to examine the spatiotemporal and causal relationship between green credit levels and enterprise green technology innovation in 271 prefecture level cities in China from 2013 to 2021. It found that (1) green credit and green technology innovation levels are both highest in the eastern region, followed by the central region, and exhibit spatial correlation characteristics. The main types of agglomeration are high–high and low–low agglomeration. (2) Green credit has a significant enhancing effect on green technology innovation in enterprises, and this conclusion still holds after robustness and endogeneity tests. (3) There is significant regional heterogeneity in the impact of green credit on green technology innovation, mainly concentrated in the central and western regions. (4) Green credit can significantly increase enterprise R&D investment and enhance the level of green technology innovation through this channel. Finally, some policy implications are provided to the decision-making departments that can be used for reference.

Spatial effects of township health centers' health resource allocation efficiency in China.

China is a large agricultural nation with the majority of the population residing in rural areas. The allocation of health resources in rural areas significantly affects the basic rights to life and health for rural residents. Despite the progress made by the Chinese government in improving rural healthcare, there is still room for improvement. This study aims to assess the spatial spillover effects of rural health resource allocation efficiency in China, particularly focusing on township health centers (THCs), and examine the factors influencing this efficiency to provide recommendations to optimize the allocation of health resources in rural China. This study analyzed health resource allocation efficiency in Chinese rural areas from 2012 to 2021 by using the super-efficiency SBM model and the global Malmquist model. Additionally, the spatial auto-correlation of THC health resource allocation efficiency was verified through Moran test, and three spatial econometric models were constructed to further analyze the factors influencing efficiency. The key findings are: firstly, the average efficiency of health resource allocation in THCs was 0.676, suggesting a generally inefficient allocation of health resources over the decade. Secondly, the average Malmquist productivity index of THCs was 0.968, indicating a downward trend in efficiency with both non-scale and non-technical efficient features. Thirdly, Moran's Index analysis revealed that efficiency has a significant spatial auto-correlation and most provinces' values are located in the spatial agglomeration quadrant. Fourthly, the SDM model identified several factors that impact THC health resource allocation efficiency to varying degrees, including the efficiency of total health resource allocation, population density, PGDP, urban unemployment rate, per capita disposable income, per capita healthcare expenditure ratio, public health budget, and passenger traffic volume. To enhance the efficiency of THC healthcare resource allocation in China, the government should not only manage the investment of health resources to align with the actual demand for health services but also make use of the spatial spillover effect of efficiency. This involves focusing on factors such as total healthcare resource allocation efficiency, population density, etc. to effectively enhance the efficiency of health resource allocation and ensure the health of rural residents.

Exploring the Integration Development Strategy of Outdoor Sports and Sports Tourism in the Background of Internet

Abstract In this section, a fusion development model of outdoor sports and sports tourism is constructed to analyze the interactions and effects of the time series variables in the fusion development model through the algorithms of time series, vector autoregression and vector error correction. Then, the spatial spillover effect is utilized to test the spatial correlation of the integrated development of outdoor sports and sports tourism, which helps the transition to test the spatial spillover effect of outdoor sports and sports tourism on economic development. Finally, the integration development strategy for outdoor sports and sports tourism is proposed, and its spatial impact is analyzed. The results show that the Log L values of the SLM model, SEM model and SDM model are 125.0624, 123.8756 and 160.9998, respectively, and the model fit R²is 0.8633, 0.8730, 0.8728 and 0.8884 respectively. Comparing the results of each spatial effect model, it is found that the use of the fixed effect SDM model has a significant impact on the development of the integration of outdoor sports and sports tourism. Spatial heterogeneity of factors affecting the integration development of outdoor sports and sports tourism is the most scientific and reasonable approach.

Perceptions of Shared decision making in cognitive impairment of LTC recipients

AbstractBackgroundShared decision‐making (SDM) plays a dominant model in healthcare systems in bringing numerous benefits to healthcare settings, healthcare providers, and patients. Promoting SDM in long‐term care (LTC) is also crucial since the LTC recipients were vulnerable to cognitive impairments and received healthcare services from diverse health professionals. This study aimed to assess the perceptions of LTC recipients toward SDM in situations of cognitive impairment.MethodA survey and one‐to‐one interview were provided to LTC recipients which explored the role of patients and family members in the decision‐making process related to their health care in the condition of cognitive impairments.ResultThirty‐eight LTC recipients participated in the SDM survey. The participants had a mean age of 77.9 (years) and were diagnosed as eligible LTC recipients for 9.01 (years). In general, most participants expected their healthcare providers to share knowledge about their diseases, and information for medical treatments. The participants described their health symptoms to their healthcare providers in as much detail as possible when they arrived at the hospital since they understood their important role in engaging decisions related to their health. In particular, 65.8% preferred to have been informed about their health conditions, and diseases. 42.1% believed that they, family member(s), and their doctor(s) should be involved together in deciding the best option(s) for their treatment. 52.6% thought that they should be played as the most important person in deciding their treatments for their own health even in those with cognitive impairments.ConclusionThis study revealed the potential of implementing the SDM model in the LTC system, especially under the condition of cognitive impairments. The LTC recipients desire to be involved in the decision‐making process related to their health even if they are in cognitive impairments. Implementing the SDM model in the LTC system potentially results in promising person‐centred care, especially for enhancing the self‐control of the recipients with cognitive impairment and their families.

Spatial pattern and driving factors of synergistic governance efficiency in pollution reduction and carbon reduction in Chinese cities

This paper is based on the construction of the evaluation system of urban pollution reduction and carbon reduction synergistic governance efficiency indicators. Firstly, we used the NDDF model to measure the efficiency of urban pollution reduction and carbon reduction synergistic governance. We then used the ArcGIS technology and spatial autocorrelation method to explore its spatial distribution pattern, and the spatial econometric model is used to explore its driving factors. The results show that from 2006 to 2020, the synergistic governance efficiency of Chinese cities in terms of pollution reduction and carbon reduction is in a state of continuous improvement, with an increase of 38.07 per cent in 2020 compared with 2006, and overall the synergistic governance efficiency of cities in terms of pollution reduction and carbon reduction shows a pattern of development in which the east is high and the west is low, but there is a tendency for the gap to narrow from one year to the next. There is a positive spatial aggregation of urban pollution reduction and carbon synergistic governance efficiency, which is further shown by the Lisa aggregation diagram that the spatial aggregation of urban pollution reduction and carbon synergistic governance efficiency in China is characterized by urban agglomerations, with the high and high aggregation zones being concentrated in the central and eastern urban agglomerations, such as the urban agglomerations of the middle reaches of the Yangtze River and Guangdong-Fujian-Zhejiang, and the low and low aggregation zones being concentrated in the western urban agglomerations, such as the Lanzhou-Western urban agglomerations and the Guanzhong Plain urban agglomerations. The results of the SDM model indicate that the efficiency of urban pollution reduction and carbon synergistic governance can significantly promote the process of pollution reduction and carbon synergistic governance in neighboring areas. The six drivers, namely, industrial structure, economic development level, innovation level, urbanization rate, population size and foreign direct investment, all have a significant impact on the efficiency of urban pollution reduction and carbon synergistic governance in a local area. The spatial spillover effects of the remaining variables, except for urbanization rate, on the efficiency of urban pollution reduction and carbon synergistic governance are significantly negative. Based on this, corresponding countermeasures are proposed to improve the synergistic governance of urban pollution reduction and carbon reduction.

Nonlinear impact and spatial effect of tourism urbanization on human settlement environment: Evidence from the Yellow River Basin, China

The human settlement environment is the concrete embodiment of measuring the sustainable development of tourism urbanization. Previous studies have preliminarily verified that there is a coupling relationship between tourism urbanization and human settlement environment. However, whether tourism urbanization can effectively improve human settlement environment, especially in the most vulnerable watershed area in China, the Yellow River Basin, is still unclear. Therefore, the purpose of this study is to explore the degree and direction of the impact of tourism urbanization on human settlement environment in 78 prefecture-level cities in the Yellow River Basin from 2005 to 2019 under the influence of nonlinear and spatial spillover effects. This paper uses the PSTR (panel smooth transition regression) model SDM (spatial Durbin) model to reveal the following results: First, tourism urbanization can significantly improve human settlement environment in the Yellow River Basin. After crossing the second threshold (0.434), the nonlinear effect of tourism urbanization on human settlement environment is the strongest, but only a very small number of cities cross the threshold. Secondly, the improvement of the level of tourism urbanization has effectively improved production environment and living environment of the region, but it has had a significant negative impact on ecological environment of the region. Finally, the combined effect of regional geographical proximity and economic interaction is conducive to the positive spatial spillover effect of tourism urbanization on production environment and living environment of surrounding areas of the Yellow River Basin. The current empirical results reveal the key role of tourism urbanization in improving human settlement environment in the Yellow River Basin, and clearly outline that the development of tourism urbanization should be consistent with the national strategy of ecological protection and high-quality development in the Yellow River Basin.

Research on the impact of green finance on China's carbon neutralization capacity.

At present, the life and production of our society are still accompanied by high pollution emissions. The proposal of the "double carbon" goal puts forward clearer requirements for China's energy conservation and emission reduction process. To cope with stricter green development requirements, green finance came into being, contributing a new path to the realization of the "double carbon" goal. This paper is based on the panel data of 30 provinces and cities in China (except Tibet, Hong Kong, Macao, and Taiwan) from 2011 to 2020, using the fixed effect model, threshold model, mediator model, and SDM model to study the impact of green finance on carbon neutralization capacity and its impact path based on reasonable measurement of regional green finance development level and carbon neutralization capacity. The study found that due to the existence of the "green paradox" and "forced emission reduction", China's green finance has a significant positive U-shaped impact path on carbon neutralization capacity; at the same time, informal regulation has a significant single-threshold effect in this path. When informal regulation crosses 0.47, the impact of green finance on carbon neutralization capacity will change from negative to positive, overcoming the impact of the "green paradox"; there is a nonlinear mediating effect of marketization level and technological innovation in the path of green finance affecting carbon neutralization capacity. In addition, green finance not only affects local carbon neutralization capacity but also has a positive spillover effect on neighboring regions.

A theoretical framework for upscaling species distribution models

AbstractSpecies distribution models (SDM) have become one of the most popular predictive tools in ecology. With the advent of new computation and remote sensing technology, high‐resolution environmental data sets are becoming more and more common predictors in these modelling efforts. Understanding how scaling affects their outputs is therefore fundamental to understand their applicability.Here, we develop a theoretical basis to understand the consequences of aggregating occurrence and environmental data at different resolutions. We provide a theoretical framework, along with numerical simulations and a real‐world case study, to show how these scaling rules influence predictive outputs.We show that the properties of the environment–occurrence relationships change when the data are aggregated: the mean probability of occurrence and species prevalence increases, the optimal environmental values shift and classification rates increase at coarser resolutions up to a certain level. Furthermore, and contrary to the widespread expectation that high‐resolution data would produce better predictions, we show here that model performance may increase using coarser resolution data sets rather than the inverse. Finally, we also show that model performance depends not only on the environment–occurrence relationship but also on the interaction between this and the geography and distribution of the available environment.This theoretical framework helps understanding previously incoherent results regarding SDM upscaling and model performance, and illustrates how theoretical and empirical results can provide important feedbacks to advance in understanding scaling issues in macroecology. The interaction between the shape of the environment–occurrence relationship and the rates of change of the environment is fundamental to understand the effects of upscaling in model performance, and may explain why some models are more difficult to transfer to different regions. Most importantly, we argue that there are conceptual choices related to scaling and SDM fitting that require expert knowledge and further explorations between theory and practice in macroecology.

Ecological footprint, natural resource rent, and industrial production in MENA region: Empirical evidence using the SDM model

Based on a spatial approach, this study aims to test and appreciate the relationship between natural resource rents, industrial production, and ecological footprint (EFP) for 17 countries in the MENA region over the period 2000–2018. Findings demonstrate the existence of (i) statical significant direct effects between environmental degradation, the level of local development, the resource rent, and the rate of industrialization. (ii) a significant positive spatial autocorrelation in EFP levels with a clear trajectory dependence characteristic in their geographic distribution. (iii) a positive interdependence between economic development, the level of industrialization, and resource rent with neighboring countries; (iv) only renewable energy conception has a negative interdependence with neighboring countries.Based on our result, regional planning can be dressed to maintain environmental quality in the region by defining the adequate compensation process between countries in the region. Developing a bio-economy seems to be a collective-collaborative process to maintain economic growth and industrial production without destroying the environment.

The impact of industrial digital transformation on green development efficiency considering the threshold effect of regional collaborative innovation: Evidence from the Beijing-Tianjin-Hebei urban agglomeration in China

Promoting industrial digitalization and deepening regional collaborative innovation is essential for achieving urban green and sustainable development. This paper constructs a differential game model based on theoretical analysis and reveals the influence mechanism of industrial digitalization and regional collaborative innovation on urban green development efficiency (GDE). After that, this paper uses the entropy method, Super-SBM model, and improved gravity model to measure the level of industrial digitalization, urban GDE, and regional collaborative innovation, respectively. Then, this paper constructs the fixed-effects model, SDM model, moderating-effects model, and threshold model, then uses the panel data of the Beijing-Tianjin-Hebei (BTH) urban agglomeration during 2011–2020 for empirical testing. The results show that: (1) industrial digitalization significantly improves urban GDE. (2) Overall, 2011–2015 is the rapid development period of BTH industry digitalization, and 2015–2020 is its stable development period. However, there are apparent spatial differences and imbalances. (3) Industrial digitalization has a significant spatial spillover effect that can enhance local and surrounding cities' GDE. (4) Regional collaborative innovation can directly enhance urban GDE, and has a positive moderating effect on industrial digitalization to enhance urban GDE. (5) The regional collaborative innovation has two thresholds of 3.9851 and 6.6184. With the collaborative innovation level improving, the main effect intensity steadily increases.

Does green finance have spatial spillover effects on carbon efficiency in China?

To meet the carbon peaking target, it is critical to understand the spatial impact of green finance. The entropy weight approach is used to create a green finance index. The SBM-GML model was used to calculate carbon efficiency. With SDM model, we analyze how green finance affects carbon efficiency.Transmission mechanism was investigated using threshold and mediation models. In terms of carbon emission efficiency, green finance has a spatial spillover effect, and green finance development can effectively enhance carbon emission efficiency improvement. The effect of green finance on carbon emission efficiency can be enhanced when PGDP and ER exceed a certain level. With a 7.6% effect size, ER played a partial mediation role.

Shared Decision-Making to Improve Health-Related Outcomes for Adults with Stroke Disease.

Stroke is one of the leading causes of disability and death worldwide, a severe medical condition for which new solutions for prevention, monitoring, and adequate treatment are needed. This paper proposes a SDM framework for the development of innovative and effective solutions based on artificial intelligence in the rehabilitation of stroke patients by empowering patients to make decisions about the use of devices and applications developed in the European project ALAMEDA. To develop a predictive tool for improving disability in stroke patients, key aspects of stroke patient data collection journeys, monitored health parameters, and specific variables covering motor, physical, emotional, cognitive, and sleep status are presented. The proposed SDM model involved the training and consultation of patients, medical staff, carers, and representatives under the name of the Local Community Group. Consultation with LCG members, consists of 11 representative people, physicians, nurses, patients and caregivers, which led to the definition of a methodological framework to investigate the key aspects of monitoring the patient data collection journey for the stroke pilot, and a specific questionnaire to collect stroke patient requirements and preferences. A set of general and specific guidelines specifying the principles by which patients decide to use wearable sensing devices and specific applications resulted from the analysis of the data collected using the questionnaire. The preferences and recommendations collected from LCG members have already been implemented in this stage of ALAMEDA system design and development.

Effects and Spatial Spillover of Manufacturing Agglomeration on Carbon Emissions in the Yellow River Basin, China

Manufacturing agglomeration is an important manifestation for cities to enhance their competitiveness, and the resource and environmental effects caused by agglomeration have become a hot topic. Based on the relevant data of prefecture-level cities in the Yellow River Basin from 2006 to 2019, this study used a Markov transition matrix to study the characteristics of carbon emission transfer and constructed an SDM model to analyze the effect of manufacturing agglomeration on carbon emissions and spatial spillover; the study drew the following conclusions: carbon emissions and the concentrations of manufacturing industries in the Yellow River Basin are on the rise, with carbon emissions showing a distribution pattern of “downstream > midstream > upstream”. Manufacturing agglomeration has a significant positive influence on carbon emissions, reflecting the necessity for the green transformation of manufacturing agglomeration. Manufacturing agglomeration has a spatial spillover effect on carbon emissions. The direct effect is positive, and the indirect effect is negative. The polarization effect caused by agglomeration weakens the development degree of neighboring areas, which may reflect the technological spillover effect of manufacturing agglomeration on neighboring areas. Manufacturing agglomeration has regional heterogeneity in carbon emissions. Compared with the middle and lower reaches of the Yellow River Basin, the effect is more obvious in the upper reaches. The study proposes countermeasures in terms of optimizing the spatial pattern of the manufacturing industry and other aspects to provide references for promoting the transformation development of the manufacturing industry in the Yellow River Basin.

Research on the Spillover Effect of Different Types of Technological Innovation on New Energy Industry: Taking China’s Solar Photovoltaic as an Example

Technological innovation has always played a very important role in the development of new energy industries. This paper takes the solar photovoltaic industry as an object of study, taking into account the diffusion of technological advances and the different roles of different technological innovations, and uses a spatial econometric SDM model to analyze the spillover effects of different types of technological advances on the solar industry in China. It is found that for the PV industry, efficiency-enhancing technological advances have the most significant impact, with efficiency-enhancing technologies contributing significantly to the annual electricity production of the PV industry; safety-enhancing technological advances having the second highest impact on the industry’s development; and cost-reducing technological advances have no significant impact on the industry. The study also found that due to the positive externalities of technological innovation, the spillover effect of technological innovation between regions has a significant impact on the development of the regional solar PV industry. In the long term, the direct effect of efficiency-enhancing technological innovation on the development of the PV industry is significantly positive, while the direct effect of safety-enhancing technological innovation on the development of the PV industry is significantly negative. Therefore, in the future, China’s solar energy industry should combine the capital investment of different types of science and technology into research and development, fully consider the impact of regional and technological spillover on industrial development, use technological innovation spillover to promote technological exchange and progress, and continuously improve the level of equipment operation safety, output efficiency, and electricity cost.

Global Value Chains, Digital Economy, and Upgrading of China’s Manufacturing Industry

This study utilized panel data from 31 provinces in China from 2006 to 2020 to investigate the impact of the digital economy on the upgrading of the manufacturing industry’s global value chain. Two types of spatial weighting matrices were used to construct SAR, SEM, SAC, and SDM models. The results revealed that technological innovation plays a direct mediating role in the upgrading of the manufacturing industry, and the global value chain has a positive regulatory effect on the relationship between the digital economy and the manufacturing industry’s upgrading. Under the economic distance spatial weighting matrix, the spatial spillover effect of the digital economy on the manufacturing industry’s global value chain is not significant, whereas, under the geographic distance spatial weighting matrix, the digital economy has a positive and significant spatial spillover effect. The SDM model showed the best explanatory effect. This implies that geographic spatial dependence has a significant impact on the upgrading of the manufacturing industry’s industrial structure, and it is positively influenced by nearby provinces. Understanding the impact mechanism and spatial spillover effects of the digital economy on the manufacturing industry’s upgrading can help promote efficient, fair, and balanced regional development. It can also aid in constructing a new domestic and international “dual circulation” development pattern that evolves with the global manufacturing value chain, sharing the dividends of the digital economy’s impact on the global value chain’s development.

Impacts of home country's institutional environment on OFDI dual margin

This study uses SDM model, SLM model and SEM model to investigate the impacts of the institutional environment of the home country on OFDI dual margin. The study reveals that there is a spatial spillover effect of the institutional environment on the dual margin. Then, The institutional environment is divided into economic environment, political environment and legal environment. The degree of financial support, legalization and corruption all have significant influence on the dual margin, but the impacts on the margin of expansion and the margin of intensification are discrepant. Furthermore, in order to analyze the regional heterogeneity of the impact of institutional environment factors on dual margin, China is divided into three regions, namely east, middle and west for sub-sample study. The study found that the impact of institutional environment does exist regional differences and spatial spillovers. Our results remain unchanged after robustness checks.

Spatial Spillover Effects of Internet Development on Foreign Trade in China

This study analyzes the spatial spillover effects of internet development on China’s foreign trade using panel data of 31 provinces in China covering 2003–2016. First, the global Moran’s I is employed to check for spatial autocorrelation in internet development. The results demonstrate a positive correlation between the internet development of the local province and the neighboring provinces during the sample period. Then, we validate the accuracy and performance of the spatial Durbin model by comparing it with two other spatial models: spatial error and spatial autoregression. The Wald and Likelihood Ratio tests confirmed the superiority of the SDM model. According to the direct and indirect effects results obtained from SDM, internet development plays an essential role in promoting local foreign trade and generates a positive spatial spillover effect on the foreign trade of neighboring provinces. The key findings suggest that China should continuously strengthen its internet infrastructure and expand its internet popularity, especially in the tertiary sector, to enhance the advantage of the internet on international trade development.

Modeling and analysis of factors affecting the integration of Beijing Tianjin Hebei low carbon economy based on SDM model

Background: Under the guidance of carbon peak and carbon neutral targets, China’s economic development is facing unprecedented challenges. It is of great strategic significance and value to study the integrated development of regional low-carbon economy. The harmonious development of Beijing-Tianjin-Hebei (Beijing-Tianjin-Hebei) has become a national strategy, and its importance is prominent.Objective: To promote the regional economic integration of Beijing-Tianjin-Hebei is the top priority of promoting the development of Beijing-Tianjin-Hebei integration at present, and is the key to achieving the coordinated development and mutual benefit of Beijing-Tianjin-Hebei.Methods: In this context, this paper studies the fuzzy control evaluation algorithm of regional economy based on SDM model. Realized the prediction of the expansion of the low-carbon economy in Beijing-Tianjin-Hebei, and put forward the development strategy of the economic integration of Beijing-Tianjin-Hebei. The ranking of regional competitiveness of fuzzy control evaluation method is in line with the reality of today’s economic expansion. The application of fuzzy rules improves the speed, accuracy and objectivity of evaluation.Results: The final experiment shows that the accuracy of the algorithm is very high, reaching 95.14%. In addition, the algorithm has higher efficiency and better performance. The fuzzy control evaluation algorithm of regional economy can better predict the economic growth of Beijing, Tianjin and Hebei.Conclusion: Propose the development strategy of Beijing-Tianjin-Hebei low-carbon economic integration to further promote the development of Beijing-Tianjin-Hebei integration and promote the adjustment and optimization of China’s economic structure.