Industrial Provinces Research Articles

Allocating quotas for industrial carbon emissions fairly and efficiently to achieve “peak carbon”: A case of the yellow river basin in China

The “peak carbon” target has triggered wide-ranging and profound systemic changes and transformations that have left the provinces along the basin facing the dual dilemma of implementing carbon reduction actions and socioeconomic development. The allocation of carbon emission quotas is a fundamental task that reflects the division of labour and collaboration within a river basin to achieve the “peak carbon” target. Therefore, the fair and efficient allocation of industrial carbon emission quotas is the key to the implementation of carbon emissions trading and the establishment of carbon offset mechanisms. In the study, based on the description of the spatial and temporal evolution of the industrial carbon footprint and its carrying capacity, the industrial carbon offset area and industrial carbon reimbursement area are divided. Moreover, the allocation method of industrial carbon emission quotas is proposed by accounting for the principles of fairness, efficiency, and sustainability, and is verified using the provinces along the Yellow River Basin in China as a case. The results show that the industrial carbon carrying capacity is “unbalanced”. The provinces with strong carrying capacity are in the middle and upper reaches, and provinces with weak carrying capacity are in the middle and lower reaches. Moreover, the industrial carbon offset in the basin as a whole is higher than the industrial carbon compensation, which is potentially conducive to the implementation of cross-basin carbon offset. The weights of the equity, efficiency and sustainability indicators are 0.46, 0.23 and 0.31, respectively. The two industrial carbon compensated provinces can transfer the surplus industrial carbon emission quotas to the two industrial carbon offset provinces after obtaining the corresponding financial compensation. The Gini coefficient is 0.26, which is relatively fair. The study not only reveals the mechanism of the relationship among industrial carbon carrying capacity, carbon emission quota and carbon offset, but also provides a scientific basis for the establishment of a carbon emission quota trading market and the strengthening of the inter-provincial coordinated emission reduction incentives in the basin.

Exploring whether digital technologies ameliorate firms’ servitization: the moderating role of organizational slack and research and development intensity

PurposeDigital technologies are essential for improving efficiency and unlocking new opportunities in various domains. The purpose of this study is to assess whether digital technologies can ameliorate servitization among manufacturing firms via the interaction of organizational slack and research and development (R&D) intensity.Design/methodology/approachDrawing on resource-based and service-dominant logic, the study employs a deductive approach and gathers empirical evidence from 1,929 listed A-shares manufacturing firms in the top-seven China mainland industrial provinces spanning the period 2012–2021. It used fixed-effect logistic regression techniques while controlling for various factors to analyze the relationship between digital technologies and manufacturing firm servitization.FindingsThe findings revealed that digital technologies significantly ameliorate manufacturing firms' servitization. Moreover, the study uncovers the contingent nature of this relationship, demonstrating that high levels of both internal and external slack, which provide flexibility and support, intensify the direction of digital technologies towards servitization. Additionally, R&D intensity reflects the firm's commitment to innovation, thereby enhancing synergistic effects in the relationship.Originality/valueThis study contributes robust and comprehensive empirical evidence that validates and establishes a clear baseline relationship reflecting the most current digital technology landscape and its implications for manufacturing firms servitization. Moreover, it provides a more patterned understanding of how internal and external slack typologies and R&D intensity contextualize our study’s findings. Additionally, it demonstrates how our theoretical synthesis advances firms’ strategic shifts towards service-oriented business models through digital technologies.

CO2 emission from the electricity sector in Iran; calculation, prediction and reduction policies

Fossil fuel power plants produce a significant amount of CO2 emissions, and this pollutant causes global warming, respiratory and heart diseases, and other significant issues. Electricity interprets as a primary and rising demand in each energy system; thus, in this paper, carbon dioxide (CO2) emission reduction was selected as the objective value for 2025. Power plant fuel consumption was surveyed to calculate the CO2 emission caused by each fuel. Also, Esfahan province (an industrial province in Iran) was investigated as the study case. Forecasting the fuel consumption for 2025 was run by two parameters: population and Gross Domestic Production (GDP), which were forecasted by the report of the Iran Statics Center and the Gaussian Process Regression (GPR) method, respectively. The CO2 emission of power plants was obtained using the coefficients of each fuel. Based on Iran's commitment to the Paris Agreement, a 4% reduction of CO2 emissions is the main objective. Thus, this study aims to reach this goal by implementing four scenarios: a) adding renewable energies, b) adding renewable energies and improving the generation efficiency, c) adding renewable energies and decreasing the grid losses, and d) combining the three scenarios mentioned above. According to these scenarios, reformation strategies compensated 10.5% of the required power, which was satisfied by renewable energies, and finally, this province can gradually satisfy a 4% reduction until 2025.

Carbon Emission Forecast Based on Multilayer Perceptron Network and STIRPAT Model

INTRODUCTION: It is of great research significance to explore whether China can achieve the "two-carbon target" on time. The MLP model combines nonlinear modeling principles with other techniques, possessing powerful adaptive learning capabilities, and providing a viable solution for carbon emission prediction.
 OBJECTIVES: This study models and forecasts carbon emissions in Jiangsu Province, one of China's largest industrial provinces, aiming to forecast whether Jiangsu province will achieve the two-carbon target on time plan and provide feasible pathways and theoretical foundations for achieving dual carbon goals.
 METHODS: Based on the analysis of the contributions of relevant indicators using the Grey Relational Analysis method, a comprehensive approach integrating the STIRPAT model, Logistic model, and ARIMA model is adopted. Ultimately, an MLP prediction model for carbon emission variations is established. Using this model, simulations are conducted to analyze the carbon emission levels in Jiangsu Province under different scenarios from 2021 to 2060.
 RESULTS: The time to reach carbon peak and the likelihood of achieving carbon neutrality vary under three scenarios. Under the natural scenario of no human intervention, achieving carbon neutrality is not feasible. While under human-made intervention scenarios including baseline and intervention scenarios, Jiangsu Province is projected to achieve the carbon neutrality target as scheduled, attaining the peak carbon goal, however, proves challenging to realize by the year 2030.
 CONCLUSION: The MLP model exhibits high accuracy in predicting carbon emissions. To expedite the realization of dual carbon goals, proactive government intervention is necessary.

High spatiotemporal resolution ammonia emission inventory from typical industrial and agricultural province of China from 2000 to 2020

As a typical industrial and agricultural province, Shandong is one of China's most seriously air-polluted regions. One comprehensive ammonia emission inventory with a high spatial resolution (1 km × 1 km) for 136 county-level administrative divisions in Shandong from 2000 to 2020 is developed based on county-level activity data with the corrected and updated emission factors of seventy-seven subcategories. Annual ammonia emissions decrease from 1003.3 Gg in 2000 to 795.9 Gg in 2020, with an annual decrease rate of 1.2 %. Therein, the ammonia emissions associated with livestock and farmland ecosystems in 2020 account for 50.8 % and 32.9 % of the provincial total ammonia emission, respectively. Laying hen and wheat are the livestock and crop with the highest ammonia emissions, accounting for 23.3 % and 36.3 % of ammonia emissions from livestock and the application of synthetic fertilizers, respectively. Furthermore, waste treatment, humans and vehicles are the top three ammonia emission sources in urban areas, accounting for 5.0 %, 4.7 % and 1.3 % of total ammonia emissions, respectively. The spatial distribution of grids with high ammonia emissions is consistent with the distribution of intensive farms. Significant emission intensity areas mainly concentrate in western Shandong (e.g., Caoxian of Heze, Qihe of Dezhou, Yanggu of Liaocheng, Liangshan of Jining) due to the large area of arable land and the high levels of agricultural activity. Overall, prominent seasonal variability characteristics of ammonia emission are observed. Ammonia emissions tend to be high in summer and low in winter, and the August to January-emission ratio is 5.6. The high temperature and fertilization for maize are primarily responsible for Shandong's increase in ammonia emissions in summer. Finally, the validity of the estimates is further evaluated using uncertainty analysis and comparison with previous studies. This study can provide information to determine preferentially effective PM2.5 control strategies.

A Study of Depopulation in Liaoning, China

As one of the most significant industrial provinces in China, Liaoning Province always plays a momentous role in modern Chinese history. For quite a few reasons, Liaoning Province has been facing the problem of depopulation in the past few decades. At the same time, this province has also been facing a severe issue of losing its local residents. It is commonly believed that the successive decrease in population in Liaoning Province is due to negative birth rates as a result of the one-child policy. However, the declining economy can be considered as the main reason for population migration in Liaoning Province, China. This paper is looking forward to discovering the reasons of depopulation based on the factors for total population and outmigration, based on analyzing the factors including reasons behind birth rates, and the drop off economy as the eldest son of the Peoples Republic of China in the past decade.

Development of Meteorological Criteria for Classifying PM2.5 Risk in a Coastal Industrial Province in Thailand

Development of Meteorological Criteria for Classifying PM2.5 Risk in a Coastal Industrial Province in Thailand

Unsustainable imbalances in urbanization and ecological quality in the old industrial base province of China

The benign interaction between ecological quality and urbanization is a crucial guarantee for regional sustainability, especially for the old industrial bases in China. However, the linkage and interplay between these two aspects have not yet been well revealed. By using the compound night light index (CNLI) and the remote sensing ecological index (RSEI), we conducted a long-term quantitative assessment of Liaoning, which is a typical old industrial base province in China. Both CNLI and RSEI have been proven to be effective indicators for monitoring and evaluating regional urbanization and ecological quality (EQ) respectively. Trend analysis methods were performed to detect the spatiotemporal dynamics of EQ. The coupling coordination degree (CCD) model is employed to estimate the linkage between urbanization and EQ. Results show that the mean RSEI of Liaoning province generally fluctuated within the range of moderate grade and displayed an oscillating upward trend from 2001 to 2020. The gaps of EQ among different cities were narrowing, and obvious clustering characteristics can be seen. According to the trend analysis findings, the area exhibiting EQ improvement over the past 20 years was larger than that showing degradation, in which forest land and cropland were the main contributors. The results from the CCD model showed that the majority of cities in the case area have not fully entered the stage of transformation development. The imbalance between urbanization and EQ is very severe and common in Liaoning province, which is mainly attributed to the relatively sluggish spatial urbanization. Additionally, the negative impact of spatial urbanization on ecological land use is not obvious in Liaoning, which indicates the relationship between urbanization and ecological quality is not always a simple negative correlation. Our results have an important implication for further understanding urbanization-induced impacts on the ecological environment and promoting sustainable development.

Spatial and Cross-Sectoral Transfer of Air Pollutant Emissions from the Fleet Electrification in China by 2030.

Fleet electrification shifts emission sources from the tailpipe to electricity generation and automotive supply chains subsequently, with emission transfer among regions. Such a spatial and cross-sectoral transfer of air pollutant emissions might embody uncertain environmental benefits spatially, which has not been comprehensively quantified, mainly due to the complexity of manufacturing processes of electric vehicle (EV) components (e.g., battery). We developed a hybrid life cycle assessment by combining inventory data of major processes and cross-sectoral input-output information and identified how China's EV deployment would influence the spatial redistribution of air pollutant emissions currently (2017) and in the future (2030). The results indicate that fleet electrification could readily reduce life cycle nitrogen oxides (NOx) and nonmethane volatile organic compound (NMVOC) emissions by 12-93%, and the reductions are estimated to be concentrated in major cities and urban agglomerations. However, increased demand for electricity and power battery production could increase PM2.5 and SO2 emissions in 17-55% of grids under all the scenarios, which emerge in coal-rich (e.g., Inner Mongolia, Shanxi) and industrial (e.g., Shandong, Henan, Jiangsu) provinces. By tracing the upstream, 31-55% of vehicle-cycle emissions are from deep supply chains but exhibit diverse sources. It suggests the necessity to relieve emissions leakage of fleet electrification by synchronizing effective environmental management across multiple sectors through EV supply chains.

Health Burden and Driving Force Changes Due to Exposure to PM2.5 and O3 from 2014 to 2060 in a Typical Industrial Province, China

While air quality in China has improved significantly in recent years, the population is becoming increasingly vulnerable to air pollution due to the aging population. In this study, we assessed premature deaths attributable to long- and short-term exposures to PM2.5 and O3, as well as their driving forces in Shandong from 2014 to 2060 based on county-level near-real-time air pollutant concentration datasets and projected concentrations of PM2.5 and O3. We found that the concentrations of PM2.5 and O3 in most districts and counties from Shandong are still higher than the corresponding concentration limit of Grade II. Premature mortality caused by long- and short-term exposures to PM2.5 decreased by 13,045 and 8092 in 2020 compared with those in 2014, respectively. Furthermore, premature mortality attributable to short-term exposure to O3 was 36.08% higher than that due to short-term exposure to PM2.5 in 2020. The results of the driving force analysis indicate that the health benefits brought about by the improvement in air quality have been offset by the changes in population age structure. In the future, the increase in O3 concentration and population aging are the top two driving forces having adverse effects on the health burden. This study provides support for controlling the health risks of PM2.5 and O3 pollution, especially for the development of dual-pollutant concentration targets and synergistic control strategies.

Co-occurrence of phthalic acid esters (PAEs) and cyclic volatile methylsiloxanes (cVMSs) in fine particulate matter (PM0.5 and PM0.1) collected from an industrial area in Vietnam

Distribution patterns of 10 phthalic acid diesters (PAEs) and four cyclic volatile methylsiloxanes (cVMSs) were investigated in fine particulate matter (PM0.1 and PM0.5) collected from Bac Ninh, an industrial province in Vietnam during September–October in 2021. Total concentrations of PAEs found in PM0.1 and PM0.5 were in the ranges of 1.76–372 (median: 34.0 ng/m3) and 2.23–895 ng/m3 (median: 15.4 ng/m3), respectively. Among PAEs, di-n-butyl phthalate (DBP) was the most abundant compound found in PM0.1, whereas, di-2-(ethyl)hexyl phthalate (DEHP) was measured at the highest concentration in PM0.5. Total concentrations of cVMSs measured in PM0.1 and PM0.5 were in the ranges of method quantification limit (MQL)–203 (median: 2.10 ng/m3) and MQL–537 ng/m3 (median: 0.389 ng/m3), respectively. Among cVMSs, decamethylcyclopentasiloxane (D5) was found at the highest concentration in both PM0.1 and PM0.5 fractions of particulate matter. The concentration ratios between PAEs and cVMSs in PM0.1/PM0.5 were greater than 1 (except di-n-octyl phthalate: DnOP), suggesting that these chemicals tend to sorb to PM0.1 more preferentially than PM0.5. Among sampling locations, high concentrations of PAEs and cVMSs were found at traffic intersections (Que Vo district) and a craft village (Tu Son city). Relatively stronger correlations existed between cVMSs pairs in PM0.1 and PM0.5 (correlation coefficient: 0.73–1) than those of PAEs (−0.83–0.90). The human exposure doses to PAEs and cVMSs through inhalation of particulate matter were estimated based on the measured concentrations in PM0.1 and PM0.5 fractions. The estimated exposure doses of PAEs and cVMSs for infants (7.1 ng/kg-bw/d and 2.5 ng/kg-bw/d) were higher than those for adults (2.6 ng/kg-bw/d and 0.9 ng/kg-bw/d).

Satellite reveals a steep decline in China's CO2 emissions in early 2022.

Response actions to the coronavirus disease 2019 perturbed economies and carbon dioxide (CO2) emissions. The Omicron variant that emerged in 2022 caused more substantial infections than in 2020 and 2021 but it has not yet been ascertained whether Omicron interrupted the temporary post-2021 rebound of CO2 emissions. Here, using satellite nitrogen dioxide observations combined with atmospheric inversion, we show a larger decline in China's CO2 emissions between January and April 2022 than in those months during the first wave of 2020. China's CO2 emissions are estimated to have decreased by 15% (equivalent to -244.3 million metric tons of CO2) during the 2022 lockdown, greater than the 9% reduction during the 2020 lockdown. Omicron affected most of the populated and industrial provinces in 2022, hindering China's CO2 emissions rebound starting from 2021. China's emission variations agreed with downstream CO2 concentration changes, indicating a potential to monitor CO2 emissions by integrating satellite and ground measurements.

Pengaruh Penerapan Sistem Pemerintahan Berbasis Elektronik Terhadap Peningkatan Kinerja Pegawai pada Dinas Perindustrian dan Perdagangan, Provinsi Sumatera Utara

The purpose of this study was to determine the effect of differences between the quantity, quality of work, timeliness and employee performance before the implementation of the Electronic Based Government System and the quantity, quality of work, timeliness and employee performance after e-government based at the Department of Industry and Trade North Sumatra Province. To analyze the research objectives, the 3 dimensions of performance contained in Dharma theory are used: namely, quantity of work, quality of work and timeliness. This study uses a quantitative approach, because in this study the relationship between variables is causal and in addition to testing the hypothesis between competency variables on performance. While the type of research used is included in the explanatory research type. namely research that is used to obtain data from certain places, using questionnaires as primary data. The results of the study found that the Implementation of Electronic-Based Government Systems gave increased performance based on the aspect of work quantity; provide performance improvement based on work quality aspects; provide performance improvement based on timeliness aspect; and provide performance improvement based on employee performance aspects.

Inter-regional economic-environmental correlation effects of power sector in China

Power sector is the pillar industry to promote China's rapid economic growth. It is also one of the sectors with the largest carbon emissions and water consumption. The green transformation of the power sector has become the focus of China's provincial economic and environmental policies. However, due to the complex inter-provincial power sector linkage, provincial power policies have spillover and overlapping effects, making it difficult for policies to coordinate and complement each other. Here, we quantify the value-added, CO2 emissions and water consumption driven by inter-provincial power sector linkage in China, and proposes differentiated policies at provincial and sectoral levels for green transformation of power sector. By quantifying the economic and environmental correlation effects among China's provincial power sectors, we found that low-carbon transformation of power sector in industrial provinces have higher economic costs. East and North China's policies committed to emission reduction in power sector can save more water.

Spatial-Temporal Evolution Characteristics of Industrial Carbon Emissions in China’s Most Developed Provinces from 1998–2013: The Case of Guangdong

Industry is widely valued as an important contributor to carbon emissions. Therefore, it is of great significance to analyze the industrial carbon emissions (ICE) in Guangdong, the strongest industrial province in China. We have adopted the carbon emission accounting model and standard deviational ellipse analysis model to analyze the temporal and spatial characteristics and evolution trends of the industry carbon emission amount and intensity in Guangdong from 1998 to 2013. The study results include: (1) Due to the rapid development of industry, Guangdong’s ICE showed a steady growth trend; (2) The distribution characteristics of ICE were characterized by the trend of taking the Pearl River Delta (PRD) region as the center and gradually spreading to the surrounding areas. From the perspective of industrial sectors, it can be divided into steady growth type, fluctuant growth type, basically stable type, and decrease type; (3) The spatial pattern of the ICE in Guangdong is basically the same as that of the total industrial output value, that is, the southwest-northeast pattern. This work is helpful for China’s carbon peak, especially for the formulation of industrial carbon peak policy and the sustainable development of the environment.

Lessons from assembling a microbial natural product and pre-fractionated extract library in an academic laboratory.

Microbial natural products are specialized metabolites that are sources of many bioactive compounds including antibiotics, antifungals, antiparasitics, anticancer agents, and probes of biology. The assembly of libraries of producers of natural products has traditionally been the province of the pharmaceutical industry. This sector has gathered significant historical collections of bacteria and fungi to identify new drug leads with outstanding outcomes-upwards of 60% of drug scaffolds originate from such libraries. Despite this success, the repeated rediscovery of known compounds and the resultant diminishing chemical novelty contributed to a pivot from this source of bioactive compounds toward more tractable synthetic compounds in the drug industry. The advent of advanced mass spectrometry tools, along with rapid whole genome sequencing and in silico identification of biosynthetic gene clusters that encode the machinery necessary for the synthesis of specialized metabolites, offers the opportunity to revisit microbial natural product libraries with renewed vigor. Assembling a suitable library of microbes and extracts for screening requires the investment of resources and the development of methods that have customarily been the proprietary purview of large pharmaceutical companies. Here, we report a perspective on our efforts to assemble a library of natural product-producing microbes and the establishment of methods to extract and fractionate bioactive compounds using resources available to most academic labs. We validate the library and approach through a series of screens for antimicrobial and cytotoxic agents. This work serves as a blueprint for establishing libraries of microbial natural product producers and bioactive extract fractions suitable for screens of bioactive compounds.

Influence Of Innovative Leadership On Work Culture And Empowerment At The Department Of Industry And Trade Maluku Province

Empowerment is an effort to build that power by encouraging, motivating and raising awareness of one's potential and to develop it. This study aims to analyze and explain: the influence of innovation leadership on work culture, the influence of innovation leadership on empowering community groups through work culture, and the influence of work culture on empowering community groups. The object of research at the Department of Industry and Trade of Maluku Province uses the path analysis method. The sampling technique used purposive random sampling. The sample in this study is the community empowerment group. The results of the study prove that: the higher the innovation leadership, the higher the work culture in the empowerment group. The higher the innovation leadership, the higher the empowerment of community groups through work culture in community empowerment groups, work culture has a strong influence to mediate innovation leadership on community group empowerment. For the next researchers, it is necessary to examine collaboration knowledge sharing, innovation leadership in the context of future community development.

Impact of thermophoretic particle deposition on heat transfer and nanofluid flow through different geometries: An application to solar energy

• Temperature, mass and flow for nanofluid with TPD and thermal radiation • Three distinct geometries (cone, wedge, and plate) are considered • Velocity decreases with improved values of magnetic and porosity parameters • Thermal distribution enhances with rising radiation parameters • The rate of mass is augmented with thermophoretic parameter increase The influence of thermal radiation adds a new dimension to the study of flow and thermal transmission in a viscous fluid across a stretched surface. Radiative effects are extremely important in engineering and physics, particularly in technology used in space and extreme-thermal operations. Thermal radiation and thermal distribution research are significant in astrophysical movements, solar power machinery, and other industrial provinces. On the other hand, the results of research on the deposition of vaporizer particles on exteriors have been useful in a variety of engineering fields. Based on the above-mentioned relevant applications, the current work examines the temperature, mass, and flow distributions in the presence of nanofluid while considering thermophoretic particle deposition (TPD) and thermal radiation over three distinct geometries (cone, wedge, and plate). By selecting appropriate similarity variables, the equations corresponding to the proposed flow are translated into ordinary differential equations (ODEs). The RKF-45 approach and a shooting system are used to evaluate the reduced equations. The important factors that affect the heat, mass, and flow profiles are deliberated with the support of graphs. Velocity profile decreases with improved values of magnetic and porosity parameters. Thermal distribution enhances with rising radiation parameters. The concentration profile improves with the thermophoresis constraint, whereas the opposite tendency is found with the thermophoretic constraint. The rate of mass distribution is augmented with improvement in the thermophoretic parameter.

Characteristics and source profiles of atmospheric volatile organic compounds (VOCs) in the heavy industrial province of Northeast China with cruise monitoring

As key precursors of tropospheric ozone (O3) pollution, volatile organic compound (VOC) pollution and related studies in China are mainly concentrated in developed regions or metropolises, while there are few studies on VOC pollution in the heavy industrial base in Northeast China. This study conducted a systematic survey of VOCs’ information for the first time in Jilin Province, covering eight prefecture-level cities, ten major industries (e.g., petrochemical, chemical), and 130 major factories, based on cruise monitoring for 2 months. The concentrations and compositions (i.e., 65 species) of VOC were measured in real-time. The emission characteristics of VOC, local source profiles, and the ozone formation potential (OFP) were further analyzed to support O3 pollution control in Northeast China. On the provincial scale, the concentrations of total VOC during the monitoring period ranged from 0.03 to 18321.5 μg/m3, and averaged at 607.9 ± 2051.8 μg/m3. Among the prefecture-level cities, Jilin City had the highest concentration level (1938.0 ± 3811.3 μg/m3) given the largest numbers of petrochemical factories, and the lowest level (85.5 ± 48.9 μg/m3) was found in Liaoyuan City. At the industrial scale, the highest (1915.2 ± 1842.6 μg/m3) was detected in the petrochemical industry with a major species of 1,1,2,2-tetrachloroethane. The lowest VOC emission concentration levels were found in the textile industry (57.8 ± 44.6 μg/m3; major species: xylene/ethylbenzene). Local source profiles in various industries were established, and normalized OFP of corresponding species was calculated, which are important parameters to evaluate the contribution of different VOC sources to O3 pollution. Combined with provincial industrial structure and future planning, we suggest two major industries including the chemical industry and petrochemical industry in Changchun City and Jilin City are prior to being optimized to reduce O3 pollution. This study contributes to the knowledge of the characteristics and source profiles of VOC emissions, providing an important reference for the management or control of O3 pollution in Northeast China.

Evaluation and Analysis of Regional Economic Growth Factors in Digital Economy Based on the Deep Neural Network

With the rise of deep learning technology, due to the superior performance of the deep neural network, its application in the digital economy has attracted extensive attention of scholars. Since the beginning of the 21st century, my country’s digital economy has developed rapidly, and its universalization and other characteristics have created favorable conditions for the optimal allocation of resources in underdeveloped regions and the exertion of comparative advantages. The digital economy will play a key role in poverty alleviation, promoting coordinated regional development, narrowing regional gaps, and improving the spatial layout of my country’s reform and opening up. This paper studies the factors that the digital economy based on deep neural networks has on regional economic growth. Simulation experiment conclusions are as follows: (1) the digital economy of Guizhou, Beijing, Chongqing, Anhui, and Tibet is growing rapidly. The central and western regions are in a period of rapid growth. For the gap between major industrial provinces, the coefficient of variation reached about 1 before 2013, then it declined rapidly, and slowed down, and steadily declined after 2019, indicating that the gap in the digital economy in various regions is narrowing in general. (2) From the national level, the digital economy index coefficient is 1.24, that is, for every 1% increase in digital economy investment, GDP will increase by about 0.24%. The labor force increased by 1% and the GDP increased by about 0.22%. This promotion effect is also very obvious. (3) Judging from the above data, the western region urgently needs to promote the construction of the digital economy and introduce high-tech digital economy talents. The talent effect in the Midwest has a significant effect on GDP. (4) From the perspective of the whole country and other regions, the parameter coefficients and signs have not changed significantly, so the original model is robust, and so the conclusion is desirable.