{"title":"Enhancing natural carbon sinks: Simulation of land use change under different carbon market scenarios by developing an ANN-ABM model","authors":"","doi":"10.1016/j.resconrec.2024.107872","DOIUrl":null,"url":null,"abstract":"<div><p>The carbon market mechanism presents an innovative approach to achieving carbon neutrality, however, its impact on regional carbon sink enhancement remains uncertain. This paper proposes an analysis framework based on an Artificial Neural Network-Agent-Based Model (ANN-ABM) to simulate the potential contribution of carbon market mechanism in enhancing carbon sinks. Taking the case of Chongming, China, the results demonstrate significant economic potential with forest land expansion from 2010 to 2020, equivalent to 30.69 % of eco-compensation. Under scenarios introducing a carbon market, there is an increased probability of converting land to high carbon sink types. Compared to the baseline scenario, carbon prices at 1.34 US$/t and 6.27 US$/t result in additional funds of 8.94 % and 41.46 %, respectively, and increase the carbon sink by 12.52 % and 30.28 %. The findings contribute to an understanding of how the value of carbon sinks can be realized through market mechanism and offer guidance for innovative eco-compensation approaches.</p></div>","PeriodicalId":21153,"journal":{"name":"Resources Conservation and Recycling","volume":null,"pages":null},"PeriodicalIF":11.2000,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Resources Conservation and Recycling","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0921344924004658","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
The carbon market mechanism presents an innovative approach to achieving carbon neutrality, however, its impact on regional carbon sink enhancement remains uncertain. This paper proposes an analysis framework based on an Artificial Neural Network-Agent-Based Model (ANN-ABM) to simulate the potential contribution of carbon market mechanism in enhancing carbon sinks. Taking the case of Chongming, China, the results demonstrate significant economic potential with forest land expansion from 2010 to 2020, equivalent to 30.69 % of eco-compensation. Under scenarios introducing a carbon market, there is an increased probability of converting land to high carbon sink types. Compared to the baseline scenario, carbon prices at 1.34 US$/t and 6.27 US$/t result in additional funds of 8.94 % and 41.46 %, respectively, and increase the carbon sink by 12.52 % and 30.28 %. The findings contribute to an understanding of how the value of carbon sinks can be realized through market mechanism and offer guidance for innovative eco-compensation approaches.
期刊介绍:
The journal Resources, Conservation & Recycling welcomes contributions from research, which consider sustainable management and conservation of resources. The journal prioritizes understanding the transformation processes crucial for transitioning toward more sustainable production and consumption systems. It highlights technological, economic, institutional, and policy aspects related to specific resource management practices such as conservation, recycling, and resource substitution, as well as broader strategies like improving resource productivity and restructuring production and consumption patterns.
Contributions may address regional, national, or international scales and can range from individual resources or technologies to entire sectors or systems. Authors are encouraged to explore scientific and methodological issues alongside practical, environmental, and economic implications. However, manuscripts focusing solely on laboratory experiments without discussing their broader implications will not be considered for publication in the journal.