Pricing climate change exposure

We estimate the risk premium for firm-level climate change exposure among S&P 500 stocks and its time-series evolution between 2005 to 2020. Exposure reflects the attention paid by market participants in earnings calls to a firm’s climate-related risks and opportunities. When extracted from realized returns, the unconditional risk premium is insignificant but exhibits a period with a positive risk premium before the financial crisis and a steady increase thereafter. Forward-looking expected return proxies deliver an unconditionally positive risk premium with maximum values of 0.5%–1% p.a., depending on the proxy, between 2011 and 2014. The risk premium has been lower since 2015, especially when the expected return proxy explicitly accounts for the higher opportunities and lower crash risks that characterize high-exposure stocks. This finding arises as the priced part of the risk premium primarily originates from uncertainty about climate-related upside opportunities. In the time series, the risk premium is negatively associated with green innovation; Big Three holdings; and environmental, social, and governance fund flows and positively associated with climate change adaptation programs

Firm‐level climate change exposure

We develop a method that identifies the attention paid by earnings call participants to firms' climate change exposures. The method adapts a machine learning keyword discovery algorithm and captures exposures related to opportunity, physical, and regulatory shocks associated with climate change. The measures are available for more than 10,000 firms from 34 countries between 2002 and 2020. We show that the measures are useful in predicting important real outcomes related to the net-zero transition, in particular, job creation in disruptive green technologies and green patenting, and that they contain information that is priced in options and equity markets

Climate Value and Values Discovery in Earnings Calls

We examine how financial analysts discuss value- and values-related climate change concerns in earnings calls. Questions on climate change frequently employ specific, and often quantitative, language, and they are tailored to both the industry in question and periods when such concerns are considered relevant. Compared to other questions, climate change questions are less about value and more about values. Both climate-related question types increased over time, with value-related questions becoming relatively more important. Climate change discussions, especially when value-related, increase a stock's trading volume, reflecting higher investor disagreement about how to interpret the information provided. Being "green'' is not an innate trait among analysts as less than 3 percent of the variance in climate change questions is due to analyst fixed effects. Analyst labor markets care about climate change questions: Both question types positively predict analysts' career trajectories, including promotions and mobility, with value queries showing a more pronounced effect. The data file is named after the date when it was created. We are still updating the paper and the data. Changes may apply. Please let us know if you identify issues or have questions about the data. We are happy to help and learn what we can improve upon

Accounting Measurement Intensity

We provide data for an empirical measure of metering problems, i.e., the difficulties of measuring productivity and rewards in firms. We build on the insight that these metering problems are reflected in the intensity with which firms apply Generally Accepted Accounting Principles when preparing their financial statements to capture economic transactions. We adapt a simple computational linguistics algorithm to identify textual patterns that uniquely signify heightened use of accounting measurement in preparation of accounting reports. The measure has been extensively validated and offers time-varying, firm-level scores of Accounting Measurement Intensity

Protective effects of Liuweiwuling tablets on carbon tetrachloride-induced hepatic fibrosis in rats

Abstract Background Liuweiwuling tablets (LWWL) are an herbal product that exerts remarkable effects on liver protection and aminotransferase levels, and they have been approved by the Chinese State Food and Drug Administration (CFDA). Clinical studies have found that LWWL can inhibit collagen production and reduce the levels of liver fibrosis markers in the serum. Thus, LWWL is expected to have beneficial effects in the treatment of liver fibrosis. The purpose of this study was to evaluate the pharmacological effects of LWWL. Methods Hepatic fibrosis was induced in rats via carbon tetrachloride (CCl4) treatment. The rats were treated twice weekly for 8 weeks with either 2 mL·kg− 1 body weight of a 50% solution of CCl4 in olive oil or olive oil alone by oral gavage. A subset of rats received daily intraperitoneal injections of either colchicine (0.2 mg/kg per day), LWWL (0.4, 1.6, or 6.4 g/kg per day), or vehicle (N = 12 for all groups) during weeks 9–12. The rats were sacrificed after 12 weeks. Pathological changes in hepatic tissue were examined using hematoxylin and eosin (H&E) and Sirius Red staining. Immunohistochemistry was performed to observe α-smooth muscle actin (α-SMA) and collagen type I (collagen I) protein expression. Western blotting was also used to detect α-SMA protein expression. Real-time quantitative reverse-transcription polymerase chain reaction (RT-qPCR) was used to detect transforming growth factor-1 (TGF-β1), platelet-derived growth factor (PDGF), tissue inhibitor of metalloproteinase-1 (TIMP1), and tissue inhibitor of metalloproteinase-2 (TIMP2) mRNA expression. Results LWWL significantly reversed histological fibrosis and liver injury, reduced the hydroxyproline content in liver tissue, and decreased α-SMA and collagen I expression. LWWL also suppressed hepatic stellate cell (HSC) activation by reducing the expression of the profibrogenic factors TGF-β1 and PDGF. The expression levels of TIMP1 and TIMP2, which regulate extracellular matrix (ECM) degradation, were decreased after CCl4 injury in LWWL-treated rats. Conclusions These data suggest that LWWL may serve as a promising therapeutic agent to reduce fibrogenesis

Temporal and Spatial Changes of Ecological Environment Quality Based on RSEI: A Case Study in Ulan Mulun River Basin, China

The Ulan Mulun River Basin is an essential ecological protective screen of the Mu Us Desert and a necessary energy base in Ordos City. With the acceleration of industrialization and urbanization, human activities have caused enormous challenges to the local ecological environment. To achieve the region’s economic sustainability and make local development plans more objective, it is necessary to evaluate the basin’s ecological environment quality over a period of time. First, in the Landsat historical images, we selected 5 years of data to investigate the changes in this time-period (2000–2020). Second, based on the opened remote sensing database on Google Earth Engine, we calculated the remote-sensing ecological index (RSEI) distribution map. RSEI includes greenness, temperature, humidity, and dryness. Thirdly, we assessed the ecological-environmental distribution and change characteristics in the Ulan Mulun River Basin. Finally, we analyzed the RSEI spatial auto-correlation distribution characteristics in the study area. The mean values of RSEI in 2000, 2005, 2010, 2015, and 2020 were 0.418, 0.421, 0.443, 0.456, and 0.507, respectively, which indicated that the ecological environment quality had gradually improved. The ecological environment quality from 2000 to 2005 had the biggest change, as the area with drastically changed water levels accounted for 78.98% of the total basin. It showed a downward trend in the central and western regions. It showed an upward trend in the eastern region. For 20 years, the area of deterioration decreased by 24.37%, and the slight change area increased by 45.84%. The Global Moran’s I value ranged from 0.324 to 0.568. The results demonstrated that the Ulan Mulun River Basin ecological environment quality spatial distribution was positively correlated, and the clustering degree decreased gradually. Local spatial auto-correlation of RSEI showed that high-high(H-H) was mainly distributed in the basin’s eastern and southern regions, where the population density was low and the vegetation was in good condition. Low-low(L-L) was mainly distributed in the basin’s central regions and western regions, where the population density was high, and the industrial and mining enterprises were concentrated. This study provided a theoretical basis for the sustainable development of the Ulan Mulun River Basin, which is crucial for the local ecological environment and economic development

Trait Variations and Probability Grading Index System on Leaf-Related Traits of Eucommia ulmoides Oliver Germplasm

Eucommia ulmoides Oliver (EUO), an economic tree grown specifically in China, is widely used in various fields. To satisfy the requirements of industrial development, superior varieties need to be selected for different uses. However, there is no unified standard for breeders to reference. In this study, leaf-related traits were classified by a probability grading method. The results indicated there were significant differences between different planting models for the studied traits, and the traits in the Arbor forest model showed more abundant variation. Compared with genotype, the planting model accounted for relatively bigger variance, indicating that the standard should be divided according to planting models. Furthermore, the optimum planting model for different traits would be obtained by analyzing the variation range. Association analyses were conducted among traits to select the crucial evaluation indexes. The indexes were divided into three grades in different planting models. The evaluation system on leaf-related traits of EUO germplasm was established preliminarily, which considered planting models and stability across years for the first time. It can be treated as a reference to identify and evaluate EUO germplasm resources. Additionally, the study served as an example for the classification of quantitative traits in other economically important perennial plants

Trait Variations and Probability Grading Index System on Leaf-Related Traits of <i>Eucommia ulmoides</i> Oliver Germplasm

Eucommia ulmoides Oliver (EUO), an economic tree grown specifically in China, is widely used in various fields. To satisfy the requirements of industrial development, superior varieties need to be selected for different uses. However, there is no unified standard for breeders to reference. In this study, leaf-related traits were classified by a probability grading method. The results indicated there were significant differences between different planting models for the studied traits, and the traits in the Arbor forest model showed more abundant variation. Compared with genotype, the planting model accounted for relatively bigger variance, indicating that the standard should be divided according to planting models. Furthermore, the optimum planting model for different traits would be obtained by analyzing the variation range. Association analyses were conducted among traits to select the crucial evaluation indexes. The indexes were divided into three grades in different planting models. The evaluation system on leaf-related traits of EUO germplasm was established preliminarily, which considered planting models and stability across years for the first time. It can be treated as a reference to identify and evaluate EUO germplasm resources. Additionally, the study served as an example for the classification of quantitative traits in other economically important perennial plants