Essays in Environmental Economics and International Trade

The first two chapters of this dissertation seek to understand how climate change affects labor market outcomes and manufacturing firms in developing country contexts. Chapter One provides worker-level evidence in Brazil on different labor market adjustment margins with respect to extreme heat shocks and the underlying transmission mechanism. Exploiting rich employer-employee matched data, I find that quarterly heat shocks lead to significant increases in the propensity of manufacturing-worker layoff through the direct labor productivity channel. A significant proportion of manufacturing workers who experienced heat-related layoffs fail to find any formal employment within 36 months. These results show that heat shocks lead to persistent negative employment effect in the formal manufacturing labor market due to failure in job transitions over the medium run. In Chapter Two, I turn the focus to manufacturing firms in Indonesia. In a heterogeneous firm model with capital-biased productivity, I incorporate temperature shocks through the direct labor productivity channel and illustrate howless productive firms decide on production and re-optimize factor intensity as temperature increases. Empirically, I match gridded daily weather data with the Indonesian firm-level industrial surveys. I find that under heat shocks, the initially less productive firms are more likely to exit, highlighting the presence of survival bias intrinsic to firm-level intensive margin analysis. Second, on the aggregate, resources reallocate from less to more productive firms within industries. Among surviving firms, we observe factor substitution from unskilled to skilled workers, and firms switching from using domestic to foreign intermediate inputs. Chapter Three investigates how global commodity price booms affect land use and forest management, and the factors that influence sustainable environmental practices of mining firms. We employ a spatial and temporal lens, by collecting proprietary data on more than 30,000 mines located around the world and matching the location of these mines to high-resolution satellite imagery. This allows a granular study of the relationship between commodity prices and loss of forest cover worldwide, as well as the spatial distribution of global mines in relation to changes in land-use patterns and local economic activities as measured by nighttime luminosity. We find a positive elasticity of forest cover loss. Mine owners from rich countries display larger disparity in the elasticity of forest cover loss when operating in low versus high income countries. Our estimates suggest that the early 2000s "commodity super-cycle" contributes to roughly 8%-20% of the observed total deforestation around mining sites and that mining-induced deforestation is not limited to the immediate surroundings of mining pits

Research on the Method and Effectiveness of College Admissions Publicity

High-quality students are an important foundation for the cultivation of talents in colleges and universities. College admissions and publicity is an important means for universities to increase the quality of new students. This article takes a university as a case study, analyzes the effectiveness of several forms of admission propagandas, and verifies it from the perspective of the quality of student source feedback. At the same time, it puts forward a new idea of college admission propaganda under the background of the new college entrance examination reform

Can Large Language Models Recall Reference Location Like Humans?

When completing knowledge-intensive tasks, humans sometimes need not just an answer but also a corresponding reference passage for auxiliary reading. Previous methods required obtaining pre-segmented article chunks through additional retrieval models. This paper explores leveraging the parameterized knowledge stored during the pre-training phase of large language models (LLMs) to independently recall reference passage from any starting position. We propose a two-stage framework that simulates the scenario of humans recalling easily forgotten references. Initially, the LLM is prompted to recall document title identifiers to obtain a coarse-grained document set. Then, based on the acquired coarse-grained document set, it recalls fine-grained passage. In the two-stage recall process, we use constrained decoding to ensure that content outside of the stored documents is not generated. To increase speed, we only recall a short prefix in the second stage, then locate its position to retrieve a complete passage. Experiments on KILT knowledge-sensitive tasks have verified that LLMs can independently recall reference passage location in various task forms, and the obtained reference significantly assist downstream tasks

Expecting Floods: Firm Entry, Employment, and Aggregate Implications

Flood events and flood risk have been increasing in the past few decades and have important consequences on the economy. Using county-level and ZIP-code-level data during 1998–2018 from the U.S., we document that (1) increased flood risk has large negative impacts on firm entry, employment and output in the long run; (2) flood events reduce output in the short run while their impact on firm entry and employment is limited. Motivated by these findings, we construct a spatial equilibrium model to characterize how flood risk shapes firms’ location choices and workers’ employment, which we use to estimate the aggregate impact of increased flood risk on the economy. We find that flood risk reduced U.S. aggregate output by 0.52 percent in 2018, 80% of which stemmed from expectation effects and 20% from direct damages. We also apply our model to studying the distributional consequences and forecasting the impact of future changes in flood risk. Our results highlight the importance of considering the adjustment of firms and workers in response to risk in evaluating the consequences of natural disasters

An Estimation of “Energy” Magnitude Associated with a Possible Lithosphere-Atmosphere-Ionosphere Electromagnetic Coupling Before the Wenchuan MS8.0 Earthquake

A large scale of abnormities from ground-based electromagnetic parameters to ionospheric parameters has been recorded during the Wenchuan MS8.0 earthquake. All these results present different anomalous periods, but there seems one common climax leading to a lithosphere-atmosphere-ionosphere electromagnetic coupling (LAIEC) right on May 9, 3 days prior to the Wenchuan main shock. Based on the electron-hole theory, this chapter attempts to estimate the “energy source” magnitude driving this obvious coupling with the Wenchuan focus zone parameters considered. The simulation results show that the total surface charges fall in ~107–108 C, and the related upward electric field is ~108–109 V/m. These corresponding parameters are up to 109 C and 1010 V/m when the main rupture happens, and the order of the output current is up to 107 A. The electric field increasing in the interface between the Earth’s surface and the atmosphere, on one hand, can cause electromagnetic parameter abnormities of ground-based observation, with the range beyond 1000 km. On the other hand, it can accumulate air ionization above pre-earthquake zone and lead to ionospheric anomaly recorded by some spatial seismic monitoring satellites

EmotionPrompt: Leveraging Psychology for Large Language Models Enhancement via Emotional Stimulus

Large language models (LLMs) have achieved significant performance in many fields such as reasoning, language understanding, and math problem-solving, and are regarded as a crucial step to artificial general intelligence (AGI). However, the sensitivity of LLMs to prompts remains a major bottleneck for their daily adoption. In this paper, we take inspiration from psychology and propose EmotionPrompt to explore emotional intelligence to enhance the performance of LLMs. EmotionPrompt operates on a remarkably straightforward principle: the incorporation of emotional stimulus into prompts. Experimental results demonstrate that our EmotionPrompt, using the same single prompt templates, significantly outperforms original zero-shot prompt and Zero-shot-CoT on 8 tasks with diverse models: ChatGPT, Vicuna-13b, Bloom, and T5. Further, EmotionPrompt was observed to improve both truthfulness and informativeness. We believe that EmotionPrompt heralds a novel avenue for exploring interdisciplinary knowledge for humans-LLMs interaction.Comment: Work in progress; 9 page

A Scorpion Defensin BmKDfsin4 Inhibits Hepatitis B Virus Replication in Vitro

Hepatitis B virus (HBV) infection is a major worldwide health problem which can cause acute and chronic hepatitis and can significantly increase the risk of liver cirrhosis and primary hepatocellular carcinoma (HCC). Nowadays, clinical therapies of HBV infection still mainly rely on nucleotide analogs and interferons, the usage of which is limited by drug-resistant mutation or side effects. Defensins had been reported to effectively inhibit the proliferation of bacteria, fungi, parasites and viruses. Here, we screened the anti-HBV activity of 25 scorpion-derived peptides most recently characterized by our group. Through evaluating anti-HBV activity and cytotoxicity, we found that BmKDfsin4, a scorpion defensin with antibacterial and Kv1.3-blocking activities, has a comparable high inhibitory rate of both HBeAg and HBsAg in HepG2.2.15 culture medium and low cytotoxicity to HepG2.2.15. Then, our experimental results further showed that BmKDfsin4 can dose-dependently decrease the production of HBV DNA and HBV viral proteins in both culture medium and cell lysate. Interestingly, BmKDfsin4 exerted high serum stability. Together, this study indicates that the scorpion defensin BmKDfsin4 also has inhibitory activity against HBV replication along with its antibacterial and potassium ion channel Kv1.3-blocking activities, which shows that BmKDfsin4 is a uniquely multifunctional defensin molecule. Our work also provides a good molecule material which will be used to investigate the link or relationship of its antiviral, antibacterial and ion channel–modulating activities in the future

Long-Horizon Dialogue Understanding for Role Identification in the Game of Avalon with Large Language Models

Deception and persuasion play a critical role in long-horizon dialogues between multiple parties, especially when the interests, goals, and motivations of the participants are not aligned. Such complex tasks pose challenges for current Large Language Models (LLM) as deception and persuasion can easily mislead them, especially in long-horizon multi-party dialogues. To this end, we explore the game of Avalon: The Resistance, a social deduction game in which players must determine each other's hidden identities to complete their team's objective. We introduce an online testbed and a dataset containing 20 carefully collected and labeled games among human players that exhibit long-horizon deception in a cooperative-competitive setting. We discuss the capabilities of LLMs to utilize deceptive long-horizon conversations between six human players to determine each player's goal and motivation. Particularly, we discuss the multimodal integration of the chat between the players and the game's state that grounds the conversation, providing further insights into the true player identities. We find that even current state-of-the-art LLMs do not reach human performance, making our dataset a compelling benchmark to investigate the decision-making and language-processing capabilities of LLMs. Our dataset and online testbed can be found at our project website: https://sstepput.github.io/Avalon-NLU/Comment: Accepted to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP, Findings of the Association for Computational Linguistics