{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:49:31Z","timestamp":1776811771693,"version":"3.51.2"},"reference-count":18,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2023,10,6]]},"abstract":"<jats:p>In order to improve the economic growth efficiency of industrial and commercial enterprises in coastal cities and realize the GDP growth of coastal cities, this paper studies the economic growth factors of industrial and commercial enterprises in coastal cities based on the unexpected super efficiency model. Based on the research and analysis of the previous economic growth theories, this paper finds out the main factors that affect the economic growth of industrial and commercial enterprises in coastal cities, and uses the advanced econometric method to establish the relevant test model to analyze the correlation between the time series of economic growth factors and the time series of coastal cities, so as to realize the economic growth factors of industrial and commercial enterprises in coastal cities Element study. The empirical results show that the main factors affecting the economic growth of industrial and commercial enterprises in coastal cities are capital and labor force, with labor force as the main body; Technical and institutional factors also contribute to the GDP of industrial and commercial enterprises in coastal cities, but the impact is not significant and needs further improvement. In general, these factors can promote the economic growth of industrial and commercial enterprises in coastal cities. The time series and time series of each factor variable are first-order non-stationary series with long-term cointegration relationship.<\/jats:p>","DOI":"10.3233\/jcm-226852","type":"journal-article","created":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T10:31:08Z","timestamp":1686306668000},"page":"2795-2809","source":"Crossref","is-referenced-by-count":0,"title":["Economic growth factors of industrial and commercial enterprises in coastal cities based on the model of unexpected super efficiency"],"prefix":"10.66113","volume":"23","author":[{"given":"Hui","family":"Chen","sequence":"first","affiliation":[{"name":"School of Engineering Management and Logistics, Shaanxi Railway Institute, Weinan, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guixian","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Business, Pingxiang University, Pingxiang, Jiangxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"55691","reference":[{"issue":"2","key":"10.3233\/JCM-226852_ref1","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1093\/ajae\/aax078","article-title":"Global economic growth and agricultural land conversion under uncertain productivity improvements in agriculture","volume":"100","author":"Lanz","year":"2018","journal-title":"Am J Agr Econ"},{"key":"10.3233\/JCM-226852_ref2","doi-asserted-by":"crossref","first-page":"106487","DOI":"10.1016\/j.ecolecon.2019.106487","article-title":"Financing coastal resilience by combining nature-based risk reduction with insurance","volume":"169","author":"Reguero","year":"2020","journal-title":"Ecol Econ."},{"key":"10.3233\/JCM-226852_ref3","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.scitotenv.2018.12.056","article-title":"Environmental regulation, economic growth and air pollution: Panel threshold analysis for OECD countries","volume":"657","author":"Xiao","year":"2019","journal-title":"Sci Total Environ."},{"issue":"162","key":"10.3233\/JCM-226852_ref4","doi-asserted-by":"crossref","first-page":"20190283","DOI":"10.1098\/rsif.2019.0283","article-title":"Machine-learned patterns suggest that diversification drives economic development","volume":"17","author":"Brummitt","year":"2020","journal-title":"J R Soc Interface."},{"issue":"sp1","key":"10.3233\/JCM-226852_ref5","doi-asserted-by":"crossref","first-page":"233","DOI":"10.2112\/SI106-055.1","article-title":"Talent demand and training strategy of oceangoing cruise company based on customized talent development model","volume":"106","author":"Wang","year":"2020","journal-title":"J Coastal Res"},{"issue":"sp1","key":"10.3233\/JCM-226852_ref6","doi-asserted-by":"crossref","first-page":"682","DOI":"10.2112\/SI103-139.1","article-title":"Finite element analysis of the influence of industrial structure on port enterprise development","volume":"103","author":"Wang","year":"2020","journal-title":"J Coastal Res"},{"key":"10.3233\/JCM-226852_ref7","doi-asserted-by":"crossref","first-page":"122518","DOI":"10.1016\/j.energy.2021.122518","article-title":"Measuring the green economic growth in China: Influencing factors and policy perspectives","volume":"241","author":"Lin","year":"2022","journal-title":"Energy."},{"key":"10.3233\/JCM-226852_ref8","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/hec.4374","article-title":"Disparate ageing: The role of education and socioeconomic gradients in future health and disability in an international context","volume":"30","author":"Atella","year":"2021","journal-title":"Health Econ."},{"issue":"3","key":"10.3233\/JCM-226852_ref9","doi-asserted-by":"crossref","first-page":"120416","DOI":"10.1016\/j.energy.2021.120416","article-title":"Modeling the impact of energy abundance on economic growth and CO2 emissions by quantile regression: Evidence from China","author":"Liu","year":"2021","journal-title":"Energy"},{"key":"10.3233\/JCM-226852_ref10","doi-asserted-by":"crossref","first-page":"106930","DOI":"10.1016\/j.ecolecon.2020.106930","article-title":"Plastic pollution and economic growth: The influence of corruption and lack of education","volume":"182","author":"Cordier","year":"2021","journal-title":"Ecol Econ."},{"issue":"1","key":"10.3233\/JCM-226852_ref11","doi-asserted-by":"crossref","first-page":"29","DOI":"10.5547\/01956574.39.1.vcou","article-title":"Long-term endogenous economic growth and energy transitions","volume":"39","author":"Court","year":"2018","journal-title":"Energy J"},{"key":"10.3233\/JCM-226852_ref12","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/j.energy.2018.01.167","article-title":"The moderating role of corruption between economic growth and CO2 emissions: Evidence from BRICS economies","volume":"148","author":"Wang","year":"2018","journal-title":"Energy"},{"issue":"pt.2","key":"10.3233\/JCM-226852_ref13","doi-asserted-by":"crossref","first-page":"2144","DOI":"10.1016\/j.rser.2017.06.025","article-title":"Urbanization, economic growth, energy consumption, and CO2 emissions: Empirical evidence from countries with different income levels","volume":"81","author":"Wang","year":"2018","journal-title":"Renew Sust Energ Rev"},{"issue":"SI","key":"10.3233\/JCM-226852_ref14","doi-asserted-by":"crossref","first-page":"450","DOI":"10.2112\/SI106-101.1","article-title":"The impact of marine environmental awareness on economic development in coastal areas","volume":"106","author":"Xu","year":"2020","journal-title":"J Coastal Res."},{"issue":"SI","key":"10.3233\/JCM-226852_ref15","doi-asserted-by":"crossref","first-page":"338","DOI":"10.2112\/JCR-SI115-102.1","article-title":"Research on forecast of coordinated development of economy and environment in coastal economic belt based on GM model","volume":"115","author":"Zhao","year":"2020","journal-title":"J Coastal Res."},{"issue":"1","key":"10.3233\/JCM-226852_ref16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1637\/11922-070918-Hist.1","article-title":"Early poultry vaccine company development: The era of entrepreneurs","volume":"63","author":"Donahoe","year":"2019","journal-title":"Avian Dis"},{"issue":"1","key":"10.3233\/JCM-226852_ref17","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1021\/acs.energyfuels.1c03396","article-title":"Key factors affecting the development of oxidative desulfurization of liquid fuels: A critical review","volume":"36","author":"Boshagh","year":"2022","journal-title":"Energy Fuels."},{"key":"10.3233\/JCM-226852_ref18","doi-asserted-by":"crossref","unstructured":"Merlin ML, Chen Y. Analysis of the factors affecting electricity consumption in DR Congo using fully modified ordinary least square (FMOLS), dynamic ordinary least square (DOLS) and canonical cointegrating regression (CCR) estimation approach. Energy. 2021; 232: 1210251-121025.11.","DOI":"10.1016\/j.energy.2021.121025"}],"container-title":["Journal of Computational Methods in Sciences and Engineering"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JCM-226852","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:07:06Z","timestamp":1776809226000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JCM-226852"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,6]]},"references-count":18,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.3233\/jcm-226852","relation":{},"ISSN":["1472-7978","1875-8983"],"issn-type":[{"value":"1472-7978","type":"print"},{"value":"1875-8983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,6]]}}}