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| DOI | 10.1051/ro/2024016 |
| Emissions Reporting Maturity Model: supporting cities to leverage emissions-related processes through performance indicators and artificial intelligence | |
| 发表日期 | 2024 |
| ISSN | 0399-0559 |
| EISSN | 2804-7303 |
| 起始页码 | 58 |
| 结束页码 | 2 |
| 卷号 | 58期号:2 |
| 英文摘要 | Climate change and global warming have been trending topics worldwide since the Eco-92 conference. However, little progress has been made in reducing greenhouse gases (GHGs). The problems and challenges related to emissions are complex and require a concerted and comprehensive effort to address them. Emissions reporting is a critical component of GHG reduction policy and is therefore the focus of this work. It is crucial to improve the process efficiency of emissions reporting in order to achieve better emissions reduction results, as there is a direct link between effective emissions policies implemented by cities and emissions reduction (or increase) due to the effectiveness of these policies. Hence, to achieve this goal, this work proposes a series of steps to investigate, search and develop performance indicators (PIs) for emissions reporting. These performance indicators are based on the data provided by cities on the processes they go through to address emission problems. PIs can be used to guide and optimize the policies responsible for implementing emission reduction measures at the city level. Therefore, the main goal of this work is two-fold: (i) to propose an emission reporting evaluation model to leverage emissions reporting overall quality and (ii) to use artificial intelligence (AI) to support the initiatives that improve emissions reporting. Thus, this work presents an Emissions Reporting Maturity Model (ERMM) for examining, clustering, and analysing data from emissions reporting initiatives to help the cities to deal with climate change and global warming challenges. The model is built using Capability Maturity Model (CMM) concepts and uses artificial intelligence clustering technologies, performance indicator candidates and a qualitative analysis approach to find the data flow along the emissions-related processes implemented by cities. The Performance Indicator Development Process (PIDP) proposed in this work provides ways to leverage the quality of the available data necessary for the execution of the evaluations identified by the ERMM. Hence, the PIDP supports the preparation of the data from emissions-related databases, the classification of the data according to similarities highlighted by different clustering techniques, and the identification of performance indicator candidates, which are strengthened by a qualitative analysis of selected data samples. Thus, the main goal of ERRM is to evaluate and classify the cities regarding the emission reporting processes, pointing out the drawbacks and challenges faced by other cities from different contexts, and at the end to help them to leverage the underlying emissions-related processes and emissions mitigation initiatives. |
| 英文关键词 | Public sector; cities; local government; Carbon Disclosure Project (CDP); climate change; global warming; emissions reporting; KPI; performance indicators; maturity model; clustering; WiSARD; ClusWiSARD; hierarchical; K-means |
| 语种 | 英语 |
| WOS研究方向 | Operations Research & Management Science |
| WOS类目 | Operations Research & Management Science |
| WOS记录号 | WOS:001197566700001 |
| 来源期刊 | RAIRO-OPERATIONS RESEARCH
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/289004 |
| 作者单位 | Universidade Federal do Rio de Janeiro; Universidade do Porto; Instituto de Telecomunicacoes |
| 推荐引用方式 GB/T 7714 | . Emissions Reporting Maturity Model: supporting cities to leverage emissions-related processes through performance indicators and artificial intelligence[J],2024,58(2). |
| APA | (2024).Emissions Reporting Maturity Model: supporting cities to leverage emissions-related processes through performance indicators and artificial intelligence.RAIRO-OPERATIONS RESEARCH,58(2). |
| MLA | "Emissions Reporting Maturity Model: supporting cities to leverage emissions-related processes through performance indicators and artificial intelligence".RAIRO-OPERATIONS RESEARCH 58.2(2024). |
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