{"title":"How to recognize the role of policy clusters in built-up land intensity: An empirical case of the Yangtze River Economic Belt of China","authors":"Shouguo Zhang , Jianjun Zhang , Anmeng Sha , Yaping Zhang , Di Zhang","doi":"10.1016/j.landusepol.2023.106909","DOIUrl":null,"url":null,"abstract":"<div><p><span>The dependence of economic and population growth on built-up land cannot be overly blamed, but it needs to be looked at closely when it goes against the principles of sustainable growth. Rapid urbanization and industrialization have led to extensive land use, and many policies have been introduced with the aim of creating clustering effects to mitigate this issue. The Yangtze River Economic Belt (YREB) of China is a typical case to make up for the fact that the role of policy clusters in built-up land intensity (BLI) is not yet fully recognized. First, the intensive use policy clusters (IUPC) of the YREB have undergone evolutionary characteristics of growth, maturity and renewal since their formal birth in 2008. Second, quantitative evidence from time-series observations and policy simulations demonstrates an effective contribution of IUPC to BLI (an average 43.1% enhancement). Worryingly, however, despite the \"lifting benefit\", the BLI is expected to show a \"decreasing trend\". Finally, the combined results are fed back to the IUPC, showing that both the abundance and heterogeneity of the policy clusters affect the impact. Specific recommendations for future </span>policy development are made based on the above results. This study provides a pragmatic and rigorous mindset and case for policy research.</p></div>","PeriodicalId":17933,"journal":{"name":"Land Use Policy","volume":"134 ","pages":"Article 106909"},"PeriodicalIF":6.0000,"publicationDate":"2023-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Land Use Policy","FirstCategoryId":"90","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0264837723003757","RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
引用次数: 0
Abstract
The dependence of economic and population growth on built-up land cannot be overly blamed, but it needs to be looked at closely when it goes against the principles of sustainable growth. Rapid urbanization and industrialization have led to extensive land use, and many policies have been introduced with the aim of creating clustering effects to mitigate this issue. The Yangtze River Economic Belt (YREB) of China is a typical case to make up for the fact that the role of policy clusters in built-up land intensity (BLI) is not yet fully recognized. First, the intensive use policy clusters (IUPC) of the YREB have undergone evolutionary characteristics of growth, maturity and renewal since their formal birth in 2008. Second, quantitative evidence from time-series observations and policy simulations demonstrates an effective contribution of IUPC to BLI (an average 43.1% enhancement). Worryingly, however, despite the "lifting benefit", the BLI is expected to show a "decreasing trend". Finally, the combined results are fed back to the IUPC, showing that both the abundance and heterogeneity of the policy clusters affect the impact. Specific recommendations for future policy development are made based on the above results. This study provides a pragmatic and rigorous mindset and case for policy research.
期刊介绍:
Land Use Policy is an international and interdisciplinary journal concerned with the social, economic, political, legal, physical and planning aspects of urban and rural land use.
Land Use Policy examines issues in geography, agriculture, forestry, irrigation, environmental conservation, housing, urban development and transport in both developed and developing countries through major refereed articles and shorter viewpoint pieces.