Pub Date : 2024-07-01DOI: 10.25082/ree.2023.01.004
Sanjay Mohite
The performance and emission characteristics of the biodiesel -- diesel mix have been assessed using the energy audit method. The goal of this commentary is to get familiar with the examination of the performance characteristics of a diesel engine fuelled with biodiesel blends using energy audit technique. An examination of heat flow, brake-specific energy consumption, friction power, and smoke has been conducted. Efficiency can be enhanced by implementing this strategy, resulting in savings of both time and energy.
{"title":"Energy audit for biodiesel performance","authors":"Sanjay Mohite","doi":"10.25082/ree.2023.01.004","DOIUrl":"https://doi.org/10.25082/ree.2023.01.004","url":null,"abstract":"The performance and emission characteristics of the biodiesel -- diesel mix have been assessed using the energy audit method. The goal of this commentary is to get familiar with the examination of the performance characteristics of a diesel engine fuelled with biodiesel blends using energy audit technique. An examination of heat flow, brake-specific energy consumption, friction power, and smoke has been conducted. Efficiency can be enhanced by implementing this strategy, resulting in savings of both time and energy.","PeriodicalId":436007,"journal":{"name":"Resources and Environmental Economics","volume":"21 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141710064","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}
Pub Date : 2024-05-07DOI: 10.25082/ree.2023.01.003
Maha Ben Jaballah, M. Ghali, Nejla Ben Arfa, Karine Daniel, G. Kleftodimos, A. Ridier
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.
{"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}
Pub Date : 2024-03-19DOI: 10.25082/ree.2023.01.002
A. Roumiani, Omid Akhgari
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.
近几十年来，全球生态足迹（EF）问题已成为环境利益相关者的严重焦虑。这种焦虑在顶级旅游吸引国更为严重。本研究的目的是研究混合效应模型和惩罚效应模型在预测八大旅游目的地国家旅游业生态足迹值方面的表现。本研究使用了世界银行和全球足迹网络数据库。使用惩罚回归和 MCMC 模型估算了过去 19 年（2000-2018 年）的 EF 值。研究结果表明，中国、法国和意大利的生态足迹数量远远高于其他国家。此外，根据研究结果，与线性回归相比，惩罚模型的性能略有提高。模型对比显示，在 Ridge 和 Elastic Net 模型中，选择的指标比 Lasso 多，但 Lasso 对生态足迹的预测性能比其他模型好。因此，使用惩罚模型仅比线性回归略好，但它们为模型的简约性提供了适当的指标选择。研究结果表明，惩罚模型是一种强大的工具，能够显著提高旅游吸引国生态足迹变量的准确性和预测性能。
{"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}
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