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René W. Aubourg David H. Good Kerry Krutilla 《Journal of policy analysis and management》2008,27(1):7-19
The environmental Kuznets curve (EKC) hypothesis conjectures a nonlinear relationship between pollution and economic growth, such that pollution per capita initially increases as countries economically develop, but then reaches a maximum point before ultimately declining. Much of the EKC literature has focused on testing this basic hypothesis and, in studies that find evidence of an EKC, estimating the “turning point” level of development at which the per capita pollution‐growth relationship changes sign. This approach has not emphasized the policy relevance of specification issues or the potential role of policy variables. This research explores a modified EKC specification which conditions the pollution‐growth relationship on a country's level of debt and degree of democratization. These variables turn out to be significant, implying that different political and economic contexts can shift EKCs and their turning points. These findings suggest that policies to relieve debt burdens and institute political reform, in addition to their usual justifications, also could be used as a strategy to reduce carbon emissions from developing countries. © 2008 by the Association for Public Policy Analysis and Management. 相似文献
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Sierra F. Kaszubinski B.Sc. Jennifer L. Pechal Ph.D. Carl J. Schmidt M.D. Heather R. Jordan Ph.D. Mark E. Benbow Ph.D. Mariah H. Meek Ph.D. 《Journal of forensic sciences》2020,65(2):513-525
Microbial communities have potential evidential utility for forensic applications. However, bioinformatic analysis of high-throughput sequencing data varies widely among laboratories. These differences can potentially affect microbial community composition and downstream analyses. To illustrate the importance of standardizing methodology, we compared analyses of postmortem microbiome samples using several bioinformatic pipelines, varying minimum library size or minimum number of sequences per sample, and sample size. Using the same input sequence data, we found that three open-source bioinformatic pipelines, MG-RAST, mothur, and QIIME2, had significant differences in relative abundance, alpha-diversity, and beta-diversity, despite the same input data. Increasing minimum library size and sample size increased the number of low-abundant and infrequent taxa detected. Our results show that bioinformatic pipeline and parameter choice affect results in important ways. Given the growing potential application of forensic microbiology to the criminal justice system, continued research on standardizing computational methodology will be important for downstream applications. 相似文献