Industrial Output Value Research Articles

Research on Adaptive Management of the Social-Ecological System of a Typical Mine-Agriculture-Urban Compound Area in North Shanxi, China.

The mine–agriculture–urban compound area formed under the combined effects of natural conditions, mineral resource endowments, and historical development is affected by severe man-made disturbances, and faces a prominent contradiction between economic development and ecological protection. Guiding the future development is an urgent problem in this region. This research used image data, logical reasoning, and empirical analysis, based on social and economic statistics and land-use data, to analyze the typical characteristics and problems of the social–ecological system in the mine–agriculture–urban compound area. Moreover, we identified future directions for the region guided by policy documents and built a philosophy framework for sustainable development and management of the region based on the concept of adaptability. The results showed the following: (1) At present, the output value of the coal industry accounts for 84.10% of the total regional output value, severely disturbing its social–ecological system, which needs to be protected and restored under human guidance and management. (2) The future development of this region depends on the one hand on green mining, and on the other hand, it is necessary to fully tap the potential of arable land and livestock farms to develop efficient and intensive agriculture. (3) The key contents of the social–ecological system management of the mine–agriculture–urban compound area include resolving the contradiction between development and protection, ensuring development, optimizing industrial structure, and safeguarding public interests. In conclusion, this research can expand the connotation and application scope of adaptive management and provide a reference for such areas facing the prominent contradiction between development and protection.

The Role of Universities in The Development of High-Tech Industry: Conceptualization and Empirical Analysis

The article examines the relationship between the development of modern universities and high-tech industry, and try to find the role of universities in the development of high-tech industries. The author takes Zhongguancun Science Park in China as a case, to establish a correlation analysis between high-tech industries and universities. The results show that the correlation between the number of graduates and the output value of high-tech industries is not significant. The correlation between the number of universities and the output value of high-tech industries is significant at the level of 0.05, with a coefficient of 0.663. Other indicators such as R&D expenditures, R&D projects, published scientific papers, patents and so on, are highly correlated with the output value of high-tech industries, while they are all significant at the 0.01 level. It is fully demonstrated that there is a high degree of correlation between high-tech industries and universities. Universities provide high-tech industries with talent and intellectual support, financial support and continuous power, the development of high-tech industries are inseparable from universities.

Comprehensive quantitative evaluation of the water resource carrying capacity in Wuhan City based on the “human–water–city” framework: Past, present and future

Given China's rapid urbanization process, the study of water resource carrying capacity (WRCC) has important theoretical and practical significance for urban sustainable development. In this paper, a “human–water–city” (HWC) framework was proposed that offers a comprehensive consideration of the development of human society, water quality and quantity, and the progress of urban construction. Based on this framework, we established an HWC evaluation indicator system and constructed the WRCC-HWC approach, to assess, compare, and forecast the WRCC. Taking Wuhan, China, as an example, principal component analysis (PCA) was adopted to evaluate the current (2009–2019) and future (2020–2030) WRCC-HWC. Specifically, global climate models (GCMs) and a grey model, GM (1,1), were used to forecast the value of the future index system. The results show that the WRCC-HWC in Wuhan gradually increased from −3.04 to 3.23 during 2009–2019, being mainly affected by the income of urban residents, per capita GDP, reuse ratio of water used by industry, built-up area, and the output value of new and high-tech industry. With a rapid growth rate from 2011 to 2016, the WRCC-HWC reached the Grade I (excellent) state in 2016. In addition, the forecast results show that, with the exceptions of precipitation and good air quality rate, all HWC indicators show upward trends from 2020 to 2030 in Wuhan. In particular, the per capita disposable income of urban residents (N1), per capita GDP (N2), and the output value of new and high-tech industry (N11) have faster growth rates, with relative growth rates from 2020 to 2030 exceeding 100%. Correspondingly, the WRCC-HWC from 2020 to 2030 were predicted to grow steadily each year, with a higher absolute growth rate starting in 2025. This study supports a comprehensive understanding of the current and future situation for WRCC-HWC in Wuhan and can provide a reference for the formulation of scientific and sustainable development strategies.

Political Sensitivity and Carbon Emissions: Evidence From a Quasi-Natural Experiment of Leadership Change in China

While there is extensive literature assessing the impact of command-and-control and market-incentive environmental regulation on carbon emissions, it overlooks the fact that command-and-control environmental regulation and market-incentive environmental regulation are the embodiment of the national concept of green development. When a national leader with a green development philosophy is in power, local governments (due to the competition among them) adjust the economic development strategies and targets in their jurisdictions according to the leaders’ philosophy, resulting in differences in urban carbon emissions. Based on Chinese urban panel data from 2010 to 2016, this study uses a difference-in-differences approach to assess the impact of a national leader with a green development philosophy in power on carbon emissions in cities with different political sensitivities and the mechanisms of the impact. Our study finds that after the change in national leadership, the carbon intensity of high politically sensitive cities decreases significantly compared to low politically sensitive cities. We further find that increasing urban green areas and limiting the development of high-polluting industries are two ways to reduce carbon emission intensity in highly politically sensitive cities. A heterogeneity test demonstrates that the higher the level of economic development of the region where the city is located, the fewer the number of industrial enterprises in the city, the lower the total industrial output value of the city, and the more domestic enterprises in the city, proving that the change of national leaders more significantly impacts the city’s carbon emission. Our study suggests that policymakers should pay careful attention to the system of appraisal of officials, urban greening and development of highly polluting industries, and differentiated environmental policies.

Carbon peaking strategies for industrial parks: Model development and applications in China

Industrial carbon emission reduction is an important target for most countries. China pledges to achieve carbon dioxide peaking and neutrality before 2030 and 2060 respectively where industrial parks agglomerate most of the manufacturing industries and contribute much to the total CO2 emission; thus, it is of great significance to explore appropriate CO2 peaking strategies for the parks. We develop a land-industry-carbon integrated model that jointly projects land availability, land productivity, industrial structure, CO2 emissions, and carbon productivity (the ratio of gross industrial output value to CO2 emission) of industrial parks to quantitatively elucidate the CO2 peaking strategies. This study applies the model to four typical medium and large-sized Chinese industrial manufacturing parks. We analyze the characteristics of energy consumptions and CO2 emissions and reveal the parks have CO2 reduction potential ranging from 52% to 73% driven by carbon productivity improvement and industrial structure adjustment. Two parks could peak CO2 emissions before 2025 and realize the absolute decoupling of economic development from CO2 emissions during 2020–2030. Their carbon productivities will be 1.54 and 1.61 times in 2025 and both 2.78 times in 2030 compared with those in 2020. Another two parks tend to step from relative decoupling during 2020–2025 to absolute decoupling later, and their CO2 emissions will peak before 2030 with 2.08- and 3.70-times carbon productivities as much as those in 2020. Based on the model applications, two synergistic carbon peaking pathways are proposed for Chinese industrial parks, including (1) reallocating CO2 caps among industries considering land and carbon productivities, (2) facilitating the carbon productivity improvement for the stock industries and setting thresholds of carbon productivity for the forthcoming incremental industries. Customized strategies are also developed for the four parks. The model can provide both a pragmatic paradigm for industrial carbon peaking and policy support for global industrial parks.

Effects of Environment and Human Activities on Plant Diversity in Wetlands along the Yellow River in Henan Province, China

Background and Objectives: The Yellow River is the sixth longest river in the world, and it is considered the mother river of China. Biodiversity conservation in the middle and lower reaches of the Yellow River is an urgent concern due to the impact of topography, sediment deposition, and human activities. Therefore, in this study, we aimed to investigate the diversity of plant communities in wetlands along the middle and lower reaches of the Yellow River from the perspectives of the natural environment and human disturbance. Materials and Methods: In this study, 830 plots were set up in seven nature reserves in the middle and lower reaches of the Yellow River to investigate wetland plant diversity. The distribution characteristics of plant diversity and the effects of environmental and human activities on plant diversity were analyzed. Results: (1) A total of 184 plant species belonging to 52 families and 135 genera were found in the seven nature reserves. Network analysis showed that the connectance index was 0.3018. (2) Betadisper analysis followed by ANOVA revealed differences in the community composition of the wetland plants (F = 21.123, p < 0.001) in the different nature reserves. (3) Analysis of variation partitioning indicated that the effects of pure environmental factors (elevation, precipitation, evaporation, and temperature) on the beta diversity of the wetland plants in the nature reserves was the strongest (15.45% and 17.08%, respectively), followed by the effects of pure human disturbance factors (population density, industrial output value, and agricultural output value) (15.13% and 16.71%, respectively). Conclusions: Variations occurred in the assemblage characteristics of the wetland plants in the different Yellow River wetland nature reserves. The wetland species exhibited strong associations with the reserves in the Yellow River wetland in Henan Province. Elevation, longitude, precipitation, and evaporation were important factors that affected the diversity of wetland plants in the middle and lower reaches of the Yellow River in China. The findings provide insights into plant biodiversity conservation in riverine wetlands.

Prediction and Trend Analysis of Regional Industrial Carbon Emission in China: A Study of Nanjing City.

Based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the impact factors of industrial carbon emission in Nanjing were considered as total population, industrial output value, labor productivity, industrialization rate, energy intensity, research and development (R&D) intensity, and energy structure. Among them, the total population, industrial output value, labor productivity, and industrial energy structure played a role in promoting the increase of industrial carbon emissions in Nanjing, and the degree of influence weakened in turn. For every 1% change in these four factors, carbon emissions increased by 0.52%, 0.49%, 0.17% and 0.12%, respectively. The industrialization rate, R&D intensity, and energy intensity inhibited the increase of industrial carbon emissions, and the inhibiting effect weakened in turn. Every 1% change in these three factors inhibited the increase of industrial carbon emissions in Nanjing by 0.03%, 0.07%, and 0.02%, respectively. Then, taking the relevant data of industrial carbon emissions in Nanjing from 2006 to 2020 as a sample, the gray rolling prediction model with one variable and one first-order equation (GRPM (1,1)) forecast and scenario analysis is used to predict the industrial carbon emission in Nanjing under the influence of the pandemic from 2021 to 2030, and the three development scenarios were established as three levels of high-carbon, benchmark and low-carbon, It was concluded that Nanjing’s industrial carbon emissions in 2030 would be 229.95 million tons under the high-carbon development scenario, 226.92 million tons under the benchmark development scenario, and 220.91 million tons under the low-carbon development scenario. It can not only provide data reference for controlling industrial carbon emissions in the future but also provide policy suggestions and development routes for urban planning decision-makers. Finally, it is hoped that this provides a reference for other cities with similar development as Nanjing.

An improved emergy ecological footprint method for ecological security assessment and quantitative analysis of influencing factors: a case study of Zhejiang Province

A scientific ecological security assessment framework is critical to regional ecological security. This paper improves the emergy ecological footprint model which optimizes the traditional emergy ecological footprint by adding the water supply service, to assess ecological security, and quantitatively analyzed the factors affecting ecological security by partial least squares regression. Zhejiang Province, one of the most developed provinces in China, was selected as a case study. The results show that (1) the emergy carrying capacity (ECC) per capita suffered a reduction from 2004 to 2018. (2) The emergy ecological footprint (EEF) per capita presented a rising trend, with a growth rate of 70.13% from 2004 to 2018. (3) The emergy ecological footprint per capita of biologically productive land has increased except for grassland and waters. (4) There was an ecological surplus in the period of 2004–2009, but an ecological deficit appeared after 2009. (5) The ecological insecurity in Zhejiang Province was increasing, which transformed from a comparably safe state to a relatively unsafe state. (6) The dominant influencing factors affecting Zhejiang’s ecological security were population density, urban built-up area per capita, secondary industry output value, etc. These findings could provide valuable decision support to policy makers in formatting policies on ecological management and sustainable regional development.

A Fuzzy Multi-Criteria Evaluation Model for the Coordination of Industrial Agglomeration and Regional Economic Growth

The coordinated development between industrial agglomeration and regional economic growth is of great significance. Based on the theory, this article constructs a fuzzy multi-criteria evaluation model for the coordination of industrial agglomeration and regional economic growth. The model reveals the impact of industrial agglomeration on economic growth by establishing a regression model. The output value of the secondary industry and the tertiary industry are classified, and the initial value method is used for dimensionless processing. The experimental results show that using the panel unit root test, panel cointegration test, panel regression analysis, and gray correlation analysis to conduct empirical analysis and research, the capital agglomeration and labor agglomeration in regional industries both promote economic growth, and the correlation degree of sustainable agglomeration components reached 89.7%, which significantly and indirectly played a role in promoting the coordinated development of economic growth in regional economic growth.

Study on the influence of COVID-19 on the growth of China's small and medium-sized construction enterprises.

The outbreak of COVID-19 at the beginning of 2020 had a significant impact on China’s economy, society, and citizens; it also had a negative impact on the development of the construction industry. In particular, small and medium-sized construction enterprises with low ability to withstand risk have been strongly impacted, aggravating a crisis of survival among these firms. The focus of this study is to analyze the impact of COVID-19 on the growth of small and medium-sized construction companies. Based on the characteristics of small and medium-sized construction enterprises, this paper establishes a growth evaluation index and builds a growth evaluation model based on factor analysis. Twenty-three construction enterprises listed on small and medium-sized enterprises board are selected as samples, and the quarterly data of 2019 and 2020 are used for empirical analysis. The results show that the epidemic has had a high short-term impact on construction enterprises, and the total output value of the construction industry in the first quarter of 2020 was 16% lower than that in the same period of last year. In the long run, the impact of the epidemic on the growth of small and medium-sized construction enterprises has been limited. In the first quarter of 2020, the growth score of enterprises decreased by only 1.95% year-over-year, and it was essentially flat in the second and third quarters. The epidemic has had little influence on solvency, tangible resources or intangible resources but a high short term influence on profitability, capital expansion and market expectations. The long-term impact is small; It is conducive to the improvement of enterprise operation ability. Finally, to both address the influence of the COVID-19 on small and medium-sized construction enterprises and to realize their transformation and upgrading, targeted suggestions are offered at the policy and enterprise levels. The results will help to understand the impact of the epidemic on the growth of construction enterprises, and provide decision support for the healthy and orderly development of the follow-up construction industry.

Underlying the influencing factors behind the heterogeneous change of urban landscape patterns since 1990: A multiple dimension analysis

Urbanization brings dramatic changes in land cover and significantly alters urban morphology, which in turn affects the urban ecological processes and environment. To promote urban sustainability, it is necessary to accurately quantify dynamic changes in urban form and reveal the associated influencing factors. This study selected seven periods of urban boundary data and socioeconomic data for 31 provincial capitals in China from 1990 to 2018. The evolution characteristics and influencing factors of urban landscape patterns in different dimensions were analyzed based on stepwise regression method. Results indicated that the patch area and aggregation of urban land showed a significant increasing trend from 1990 to 2018, whereas the landscape fragmentation exhibited a significant decreasing trend. The shape complexity of each city showed an increasing trend from 1990 to 2005, and that of eastern cities and megacities tended to decrease thereafter. This suggest that when urban development reaches a certain level, landscape shape will gradually become regular. There was obvious spatial heterogeneity in the influencing factors of urban landscape pattern evolution. Social and economic development promoted urban expansion whereas reduced urban fragmentation, resulted a negative effect on patch density. For the economically developed cities in the east, after 2005, the urban form has tended to be regularized, and the further increase of the output value of tertiary industry will reduce the landscape shape index (LSI). Urbanization rate played major role in contagion for megacities and other cities were mainly influenced by fixed-asset investment. Therefore, heterogeneous measures should be considered for future urban landscape pattern optimization at various economic zone or hierarchical city scales.

Evaluation of Low-Carbon Scientific and Technological Innovation-Economy-Environment of High Energy-Consuming Industries

The coordination of scientific and technological innovation with economy and environment is conducive to the sustainable development of high energy-consuming industries. Under the background of realizing the “carbon peak and neutrality” goal in China, this paper constructs the evaluation index system of scientific and technological innovation, economy, and environment of high energy-consuming industries. Based on the coupling coordination theory, this paper analyzes the coordinated development of scientific and technological innovation, economy, and environment of high energy-consuming industries from 2011 to 2019 and analyzes the factors restricting the coordinated development of the three systems. The results show that with the emphasis on scientific and technological innovation and ecological environment, the coordination degree of the complex system of scientific and technological innovation, economy, and environment of high energy-consuming industries is gradually increasing. R & D investment, the proportion of total industrial output value in GDP, and coal consumption per 10000 yuan of industrial output value are the main influencing factors of the coordination of the three systems.

The Influence of Population Agglomeration on the Upgrading of the Industrial Structure — Evidence from China

As an important component of economic, social, and industrial development, changes in the labor force affect a country’s economic development in terms of fiscal expenditure, consumption structure, and production capacity, which also promote or inhibit the upgrading of the overall industrial structure and internal structure. Based on the inter-provincial panel data of 30 provinces, autonomous regions, and municipalities (excluding the Tibet Autonomous Region) in mainland China from 2010 to 2019, this paper conducts an empirical analysis of the impact of population agglomeration on the upgrading of the industrial structure and the contribution rate of tertiary industry output value in different regions. We conclude: (1) Generally speaking, population agglomeration has the effect of promoting the upgrading of the industrial structure. (2) The impact of population agglomeration on the industrial structure is different in different regions, hindering the upgrading of the industrial structure in eastern provinces, having no significant impact on the western region, and encouraging the upgrading of the industrial structure of central provinces. (3) Concerning the tertiary industry, population agglomeration significantly promotes the contribution rate of the tertiary industry output value in different regions. Therefore, the following suggestions are put forward: (1) Promote the reform of the household registration system. (2) Increase investment in human capital.

Information and Data Analysis Based on Big Data and Blockchain Technology in Promoting the Development of Cultural Tourism Industry

With the introduction of the reform and opening-up policy, the Chinese tourism industry has made considerable progress, but the current development is facing new development shortcomings and limitations. With the rapid development of technology and informatization, big data and blockchain technology provide new ideas and directions for the development of information and data analysis in the cultural and tourism industry. Under big data and blockchain technology, an in-depth research is conducted on the statistical system and GDP accounting data of the two industries of culture and tourism using coupling coordination model, model setting, and proxy re-encryption technology. It coordinates the statistical system of cultural and tourism industries, forming a unified cultural and tourism statistical report and value-added accounting report. It uses blockchain technology to calculate and analyze that the total output value of the tourism industry and cultural integration development in the past two years has increased by 1,4677.5 billion yuan, with a growth rate of 42.79%. Under the integrated development of culture and tourism in various regions, the overall income has shown an upward trend, with the highest growth rate of nearly 50%. The deep integration and development of cultural and tourism industry will become the top priority of Chinese cultural and tourism work in the future. With the addition of big data and blockchain technology, the great development of cultural and tourism integration has been realized.

Impact of Green Finance and Environmental Regulations on the Green Innovation Efficiency in China

Innovation is the first driving force for development, and green innovation efficiency (GIE) plays a very important role in regional sustainable development. Data from 31 provinces and cities in China from 2011 to 2020 were used to select the proportion of energy saving and environmental protection costs in GDP as the green financial value, and the proportion of industrial pollution control input in GDP as the environmental regulation index. Green innovation efficiency is measured from two aspects of input and output by DEA method, and carried out for 31 provinces and cities in three regions. Using the DEA-Malmquist index to measure regional green innovation efficiency, the results show that the green innovation efficiency in three regions basically presents an upward trend, but the upward trend of green innovation efficiency is different between the three regions. A Tobit regression model is constructed to explore the impact of green finance and environmental regulations on the green innovation efficiency in these three regions. Research indicates that environmental regulations, the proportion of output value of tertiary industry in GDP, industrial structure, and foreign direct investment have significant impacts on the green innovation efficiency in all regions. Green finance, industrial structure, and power consumption have a significant impact on the green innovation efficiency in eastern China. Industrial structure has a significant impact on green innovation efficiency in central China, while power consumption and industrial structure have a significant impact on green innovation efficiency in western China. Therefore, each region needs to improve the standard of environmental regulation innovation, and introduce and use foreign investment in a scientific and reasonable way so as to promote the improvement of industrial infrastructure.

Analysis of Ecological Environment Changes and Driving Forces in Boao town of Hainan Province

Based on the three phases of remote sensing imageries in 2000, 2010 and 2020, taking Boao town as the study area, this paper analyzed the changes in the ecological environment and the driving forces of ecological environment. The results showed that: (1) The main land use type of Boao town was forest, followed by cultivated land. The main change of land use types in Boao town from 2000 to 2020 is from forest to other land types. (2) The average vegetation coverage of Boao town was 70.68% in 2000, 62.49% in 2010 and 71.65% in 2020, which indicated a trend of first decreasing and then increasing. (3) The changes of landscape pattern index of type level from 2000 to 2020 were as follows: the landscape shape index and landscape fragmentation of cultivated land decreased gradually, while the landscape shape index and landscape fragmentation of construction land and forest land increased. The trend of landscape pattern index of landscape level from 2000 to 2020 was as follows: landscape fragmentation continued to increase, landscape spread continued to decline, landscape separation continued to rise, and landscape diversity and evenness continued to increase. (4) In the driving force analysis of ecological environment changes in Boao Town, the results of principal component analysis showed that human factors were the leading factors of ecological environment changes in Boao Town, and economic factors such as GDP, output value of the third industry and per capita disposable income of farmers were the main driving forces of landscape pattern changes in Boao town.

The DPSIRM - Grey cloud clustering method for evaluating the water environment carrying capacity of Yangtze River economic Belt

Water Environment Carrying Capacity (WECC) is a complex system affected by society, economy, population and other factors. In this study, “Driving force- Pressure –State - Influence - Response –Management” (DPSIRM) method is introduced to analyze regional WECC, that is, WECC system is classified into six subsystems to illustrate its internal mechanism, establishing a systematic evaluation index system affecting WECC. The Fuzzy Analytic Hierarchy Process (FAHP)-Grey cloud clustering method is developed for making comprehensive WECC evaluation. Three major regions composed of 11 provinces in the Yangtze River Economic Belt (YREB) of China are selected as the study case. With regard to subsystem weights, the results show that pressure and state subsystems occupy a leading position on the whole system. For indicator weights, there are significant differences among them, and the top 5 indicators in importance are respectively: annual average total phosphorus (TP) concentration, soil erosion control rate, sediment delivery modules, wastewater effluent amount of industrial output value per 10,000 Yuan, and population density. In addition, through analyzing synthetic evaluation results, it can be found that WECC levels of different provinces present obvious spatial heterogeneity on each subsystem and overall system along the YREB, that is, they decrease along the Yangtze River from west to east except for minor deviations. Meanwhile, comprehensive scores of each regional WECC show an overall increasing trend from year 2014 to 2019, displaying significant temporal variations. Through making sensitivity analysis for WECC index system, it can be concluded that the most sensitive indicators of each subsystem are Population density, Wastewater effluent amount of industrial output value per 10,000 Yuan, Annual average TP concentration, Sediment delivery modules, Soil erosion control rate, Green coverage rate of built-up areas, respectively, which also turned out to be the top six sensitivity indicators among the whole index system. Finally, the demonstration conducted proves that the introduced DPSIRM - Grey cloud clustering method is applicable and effective to evaluate WECC at regional level.

ФАКТОРЫ, ВЛИЯЮЩИЕ НА ГЕОГРАФИЧЕСКОЕ РАСПРЕДЕЛЕНИЕ ЗАГРЯЗНЯЮЩИХ ПРОМЫШЛЕННОСТЕЙ

The paper has verified the hypotheses and theories about the location selection of pollution-intensive industries from various perspectives and explored possible factors influencing the geographical distribution of pollution-intensive industries. Based on the pollution intensity index of each industry, we firstly identified the pollution-intensive industries in China. And the geographical distribution of intensive industries is analysed in terms of the proportion of industry, the industry output value and the number of enterprises. With the regression analysis, the factor endowment, environmental regulation and globalization are proven factors influencing the geographical distribution of pollution-intensive industries.

Carbon Emission Forecast of Construction Industry Based on Grey Theory

In order to reduce emissions of construction industry in China at an early date, this paper provides a fitting of different influencing factors based on the grey correlation method from the construction industry perspective. The correlation degree of carbon emission and related factors in construction industry was ranked as Rate of technical equipment > Practitioners> GDP > Population size > Gross output value of construction industry. And the future of carbon emissions forecast through the grey forecast, found that the country's construction industry will emit more than 80 million tons of carbon in 2030, facing very great pressure to reduce emissions. Therefore, the construction industry should realize the innovation potential of technology and industry model, accelerate the energy transition and achieve the inter-country emission reduction target at an early date.

Analysis on the Adjustment of Marine Economic Industrial Structure Based on the Least Square Regression Model

In order to intuitively analyze and clearly understand the effect of industrial structure adjustment of the marine economy in recent years in China, modeling and empirical research on industrial structure adjustment of the marine economy are carried out. Three comparisons of marine economic industrial structure and per capita output value coefficient of the marine industry are selected as scientific measurement indexes, the ratio of regional entropy to the output value of marine scientific research management service industry is selected as the height analysis index, and the econometric model is constructed according to the influence of the scientificity and advancement of marine economic industrial structure on marine economic GDP. Based on the empirical analysis of the relevant economic data of China’s marine industry from 2008 to 2018, the model variable stationarity test and least squares regression analysis showed that the fitting degree of the model was good and significant; with every 1% increase in the scientificity and height of marine economic industrial structure adjustment, the marine economy increased by 1.88% and 2.80%, respectively. The development of marine science and technology can promote the rise of the marine economy. According to the model, the adjustment strategy of the marine economy industrial structure is made to further promote the development of the marine economy.