How to make value-driven climate science for policy more ethical

In previous works, I examine inferential methods employed in Probabilistic Weather Event Attribution studies (PEAs), and explored various ways they can be used to aid in climate policy decisions and decision-making about climate justice issues. This paper evaluates limitations of PEAs and considers how PEA researchers’ attributions of “liability” to specific countries for specific extreme weather events could be made more ethical. In sum, I show that it is routinely presupposed that PEA methods are not prone to inductive risks and presuppose that PEA researchers thus have no epistemic consequences or responsibilities for their attributions of liability. I argue that although PEAs are nevertheless crucially useful for practical decision-making, the attributions of liability made by PEA researchers are in fact prone to indicative risks and are influenced by non-epistemic values that PEA researchers should make transparent to make such studies more ethical. Finally, I outline possible normative approaches for making sciences, including PEAs, more ethical; and discuss implications of my arguments for the ongoing debate about how PEAs should guide climate policy and relevant legal decisions.     

New tools for theory choice and theory diagnosis

Theory choice can be approached in at least four ways. One of these calls for the application of decision theory, and this article endorses this approach. But applying standard forms of decision theory imposes an overly demanding standard of numeric information, supposedly satisfied by point-valued utility and probability functions. To ameliorate this difficulty, a version of decision theory that requires merely comparative utilities and plausibilities is proposed. After a brief summary of this alternative, the article illustrates how comparative decision theory affords a rational reconstruction of decisions made by exemplary scientists in two cases of theory choice: Buffon’s law and the luminiferous ether. It also offers a rational reconstruction of two cases of theory diagnosis: Mendeleev’s anomalies and the Pioneer anomaly.     

Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors

In this paper, we assess the predictive content of latent economic policy uncertainty and data surprise factors for forecasting and nowcasting gross domestic product (GDP) using factor-type econometric models. Our analysis focuses on five emerging market economies: Brazil, Indonesia, Mexico, South Africa, and Turkey; and we carry out a forecasting horse race in which predictions from various different models are compared. These models may (or may not) contain latent uncertainty and surprise factors constructed using both local and global economic datasets. The set of models that we examine in our experiments includes both simple benchmark linear econometric models as well as dynamic factor models that are estimated using a variety of frequentist and Bayesian data shrinkage methods based on the least absolute shrinkage operator (LASSO). We find that the inclusion of our new uncertainty and surprise factors leads to superior predictions of GDP growth, particularly when these latent factors are constructed using Bayesian variants of the LASSO. Overall, our findings point to the importance of spillover effects from global uncertainty and data surprises, when predicting GDP growth in emerging market economies.     

Human bodies as chemical sensors: A history of biomonitoring for environmental health and regulation

The testing of human blood and urine for signs of chemical exposure has become the “gold standard” of environmental public health, leading to ongoing population studies in the US and Europe. Such methods first emerged over a century ago in medical and occupational contexts, as a means to calibrate drug doses for patients and prevent injury to workers from chemical or radiation exposure. This paper analyzes how human bodies have come to serve as unconscious sensors of their environments: containers of chemical information determined by expert testers. As seen in the case of lead testing in the US, these bodily traces of contaminants can provide compelling evidence about dangerous exposures in everyday life, useful in achieving stronger regulation of industry. The use of genetic testing of workers by Dow Chemical provides an example of industry itself undertaking biomonitoring, though the company discontinued the program at the same time its studies indicated chromosomal damage in connection with occupational exposure to certain chemicals. In this case and others, biomonitoring raises complex questions about informing subjects, interpreting exposure in the many cases for which health effects at low doses are unknown, and who should take responsibility for protection, compensation, or remediation. Further, the history of biomonitoring complicates how we understand human ‘experience’ of the global environment by pointing to the role of non-sensory—yet detectable—bodily exposures.     

民营水保——金沙江干热河谷水土保持建设的新兴模式

民营水保已发展成为金沙江干热河谷地区——云南省元谋县水土保持生态建设和农村经济发展的一支重要的生力军。民营水保一改过去“国家出钱、部门包办、群众出力”的水保建设传统模式,通过积极吸引民间资本,以市场为导向进行生产经营,有效解决了以往水保建设资金不足、水保工程破坏严重、管护困难、经济效益低下等长期困扰水保建设的问题,经济效益突出,水保成效显著。本文在回顾民营水保在云南省元谋县的产生背景、发展历程的基础上,重点介绍民营水保的主要发展形式及其特点、发展中存在的问题。元谋的水保实践表明,民营水保较好地将经济效益与生态效益结合起来,是一种具有发展前景的水土流失治理及经济发展的新兴模式,为新形势下金沙江干热河谷地区水保建设的发展提供了一条新的思路。