Resources and Environmental Economics最新文献

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Energy audit for biodiesel performance 生物柴油性能的能源审计

Resources and Environmental Economics Pub Date : 2024-07-01 DOI: 10.25082/ree.2023.01.004

Sanjay Mohite

引用次数: 0

Farm digital tools: A systematic review of investments and environmental implications 农场数字化工具：对投资和环境影响的系统审查

Resources and Environmental Economics Pub Date : 2024-05-07 DOI: 10.25082/ree.2023.01.003

Maha Ben Jaballah, M. Ghali, Nejla Ben Arfa, Karine Daniel, G. Kleftodimos, A. Ridier

{"title":"Farm digital tools: A systematic review of investments and environmental implications","authors":"Maha Ben Jaballah, M. Ghali, Nejla Ben Arfa, Karine Daniel, G. Kleftodimos, A. Ridier","doi":"10.25082/ree.2023.01.003","DOIUrl":"https://doi.org/10.25082/ree.2023.01.003","url":null,"abstract":"Farm-level investment in digital tools is often viewed as a necessary part of the agroecological transition. However, its actual relevance remains unclear due to currently ambiguous definitions of farm investments in general and equipment investments in particular. We conducted a systematic review of the farm investment literature to characterize the different categories of digital tools investments seen and to determine how often the environment is considered in this field of research. A total of 131 articles met our eligibility criteria and were subject to further analysis. First, we found that research on farm investments has looked at general farm investments, investments in combined factors of production, and investments in specific factors of production. Second, we discovered that there are four main investment categories for farm equipment (including digital tools). Third, we noted that few studies have addressed the environmental implications of investing in digital tools. Our findings emphasize that, to facilitate the agroecological transition, it will be important to promote broader strategies that encourage farmers to invest in digital tools.","PeriodicalId":436007,"journal":{"name":"Resources and Environmental Economics","volume":"133 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141002933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The performance of mixed and penalized effects models in predicting the value of the ecological footprint of tourism 混合效应模型和惩罚效应模型在预测旅游业生态足迹价值方面的表现

Resources and Environmental Economics Pub Date : 2024-03-19 DOI: 10.25082/ree.2023.01.002

A. Roumiani, Omid Akhgari

{"title":"The performance of mixed and penalized effects models in predicting the value of the ecological footprint of tourism","authors":"A. Roumiani, Omid Akhgari","doi":"10.25082/ree.2023.01.002","DOIUrl":"https://doi.org/10.25082/ree.2023.01.002","url":null,"abstract":"In recent decades, the issue of ecological footprint (EF) in the world has become a serious anxiety among environmental stakeholders. This anxiety is more in top tourism attracting countries. The purpose of this research is the performance of mixed and penalized effects models in predicting the value of the EF of tourism in the top eight countries of tourism destinations. The World Bank and Global Footprint Network databases have been used in this study. Penalized regression and MCMC models have been used to estimate the EF over the past 19 years (2000-2018). The findings of the research showed that the amount of ecological footprint in China, France and Italy is much higher than other countries. In addition, based on the results, a slight improvement in the performance of penalized models to linear regression was observed. The comparison of the models shows that in the Ridge and Elastic Net models, more indicators were selected than Lasso, but Lasso has a better predictive performance than other models on ecological footprint. Therefore, the use of penalized models is only slightly better than linear regression, but they provide the selection of appropriate indices for model parsimoniousness. The results showed that the penalized models are powerful tools that can provide a significant performance in the accuracy and prediction of the EF variable in tourism attracting countries.","PeriodicalId":436007,"journal":{"name":"Resources and Environmental Economics","volume":"17 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140229564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0