In an era where wildfires have intensified in frequency and ferocity, comprehending their profound economic impact has assumed critical importance. This empirical tour de force, fortified by meticulous spatial econometric analysis, not only advances our understanding but also equips policymakers, businesses, and regions with vital insights.

This study presents a holistic examination of the economic reverberations of wildfires in California spanning the past two decades. With a granular focus ranging from top 2-digit to the precise 6-digit NAICS sector level, it scrutinizes sectors directly and indirectly affected by these environmental upheavals. Our approach melds Geographic Information System (GIS) data, detailing wildfire frequency and intensity, with a comprehensive, privately acquired dataset encompassing annual geocoded records of over 90 million U.S. businesses. These records include vital information such as sales performance and employment figures.

Our empirical journey navigates the multifaceted impact of wildfires, unveiling their influence on establishment births, deaths, in-migrations, out-migrations, employment dynamics, and sales performance. The results paint a vivid picture of nuanced economic transformations across sectorial landscapes. Some sectors experience a surge in establishment deaths and outward migrations, correlating with wildfire frequency, while others witness a paradoxical phenomenon—increased sales performance driven by wildfire intensity, albeit accompanied by a reduction in establishment births.

These findings transcend the academic realm, carrying substantial implications for policy formulation, strategic business decisions, and regional development initiatives. They underscore the imperative for adaptive strategies in the face of evolving environmental challenges.

For detailed results and deeper insights, please feel free to reach out. This research, a testament to the power of empirical analysis and spatial econometrics, stands as a beacon for those seeking to understand and navigate the intricate interplay between wildfires, economic resilience, and labor market dynamics.

In the realm of environmental economics, our research delves into a critical but underexplored dimension— the economic impact of delisting species from the endangered and threatened categories under the Endangered Species Act (ESA). While extensive studies have probed the consequences of species listings, our work fills a significant gap by shedding light on the aftermath of delisting. The implications extend far beyond academic curiosity, offering actionable insights to a diverse spectrum of stakeholders, from business entrepreneurs and environmental agencies to government policymakers. Biodiversity preservation transcends ecological concerns; it intricately weaves into socio-economic fabric. Species listings under the ESA often entail stringent regulations that influence land use, resource management, and development. These measures can ripple through local economies, impacting businesses, property values, and regional prosperity. The subsequent delisting of species post-recovery or extinction heralds a pivotal moment. Development restrictions within their critical habitats are typically lifted, potentially unlocking new economic opportunities for businesses. The stakes are undeniably high. Misguided or incomplete comprehension of these dynamics can lead to policy decisions that inadvertently harm both the economy and the environment. Our research, grounded in rigorous spatial econometric analysis techniques and enriched by Geographic Information System (GIS) tools, aims to provide clarity. By comprehensively unraveling the short-term and long-term economic ramifications of delisting, we empower decision-makers with vital information for crafting informed, sustainable policies. Amidst mounting global pressures on ecosystems, this study serves as a timely beacon, illuminating the path toward balanced and sustainable economic development. A robust empirical analysis and innovative GIS insights sets the stage for a comprehensive understanding of how delisting endangered species can influence economic trajectories.

"EcoBiz Forecast" emerges as a roundbreaking endeavor, pioneering the application of Machine Learning Modeling Techniques to predict the intricate movements of business establishments across the United States. With a rich and extensive dataset comprising geocoded information on over 90 million U.S. businesses, this research venture stands at the forefront of empirical economics, poised to unveil a new era of economic forecasting. This study harmonizes data from an array of sources, encompassing the Environmental Protection Agency's (EPA) Greenbook, which offers valuable insights into county-level non-attainment status for diverse criteria pollutants. It seamlessly integrates this data with information on protected areas from the PAD-US geodatabase, all while incorporating critical data from the U.S. Census Bureau and the Bureau of Labor Statistics. While this paper remains a work in progress, its implications are profound. Through the meticulous application of advanced econometric and machine learning methodologies, it endeavors to provide forecasts that transcend the limitations of conventional economic models. By unraveling the complex relationships between business establishment dynamics, environmental sustainability, and regulatory frameworks, this research seeks to offer actionable intelligence to policymakers, industry leaders, and environmental advocates. As the study progresses, it continues to fine-tune its methods, leveraging innovative modeling techniques and geo-spatial data analysis. Anticipate forthcoming insights that promise to reshape our understanding of the intricate tapestry woven by economic activities, environmental preservation, and regulatory compliance within the United States.

Within the intricate tapestry of land use and conservation, this research delves into the multifaceted relationship between protected area designations (PAD) and the behavior of land-intensive (LI) plants at the county level in the United States. This study leverages a comprehensive panel dataset spanning two decades (1998-2018) to meticulously examine the births, deaths, and relocations of LI plants across all U.S. counties and states. This analysis extends beyond mere observation, considering a nuanced interplay of socio-economic and environmental factors at the county level. Empirical findings reveal a captivating non-linear effect of PAD on the spatiotemporal dynamics of LI plant locations. It is not simply the presence or absence of PAD that matters, but the extent and configuration of such designations within a county. Strikingly, this research demonstrates that PAD significantly influences capital flows within the LI sector exclusively through plant exits, rather than entries. A distinctive inverted U-shaped relationship is unveiled, indicating that as the coverage of PAD within a county expands, the number of plant deaths and total plant exits initially rise, only to subsequently decline. Furthermore, the exploration of inter-state outward relocations uncovers a compelling U-shaped association with PAD. This nuanced finding suggests that while certain levels of PAD may encourage outward relocations of LI plants, an excessive concentration of protected areas can lead to a reversal of this trend. The implications of our research reverberate across the spectrum of stakeholders, from policymakers tasked with designing effective conservation policies to land-intensive businesses seeking to navigate the consequences of such policies. By untangling the intricate web of factors governing LI plant behavior in the context of protected areas, this study equips decision-makers with vital insights for crafting nuanced, sustainable conservation strategies.