County Level Research Articles

Income and racial disparity in household publicly available electric vehicle infrastructure accessibility

Publicly available electric vehicle (EV) infrastructure is pivotal for the United States EV transition by 2030. Existing infrastructure lacks equitably distribution to low-income and underrepresented communities, impeding mass adoption. Our study, utilizing 2021 micro-level data from 121 million United States households, comprehensively examines income and racial disparities in EV infrastructure accessibility. Our analysis of national averages indicates that lower-income groups face less accessibility to public EV infrastructure in both urban and rural geographies. Black households experience less rural accessibility, but greater urban accessibility compared to White households conditioning on income. However, our localized analysis uncovers significant variations in accessibility gaps among counties, rural and urban settings, and dwelling types. While Black households experience greater urban accessibility nationally, a closer look at the county level reveals diminishing advantages. This study identifies areas with pronounced inequality and urgent needs for enhanced accessibility, emphasizing the necessity for tailored solutions by local governments to enhance equitable access to EV infrastructure.

From poverty to prosperity: assessing of sustainable poverty reduction effect of “welfare-to-work” in Chinese counties

The “welfare-to-work” program is a comprehensive supportive policy in the 14th five-year plan period in China. In this paper, a systematical assessment of the long-run effectiveness of the welfare-to-work policy on poverty reduction is of great significance to stimulate the internal impetus of people who are lifted out of poverty to achieve income growth and prosperity and promote regional economic development. Based on the data at the county (city) level in China from 2000 to 2019 and the sustainable development theory, in this paper, a county-relative poverty evaluation system was constructed. Besides, the double difference method was employed to evaluate the effect of the welfare-to-work policy on poverty reduction and test its action mechanism. The findings are as follows: (1) the welfare-to-work policy has a significant poverty reduction effect and presents an inverted “U” shape. In addition, significant achievements have been made in “maintaining employment stability, ensuring income, strengthening skills, and supporting the economy” ; (2) the welfare-to-work policy boosts poverty reduction in counties through infrastructure construction, fiscal intervention, and financial tools; however, the financial tools play a positive role in poverty reduction in the northwest region and suppressed role in the southwest region, and has an insignificant effect in the central region; and (3) there are differences in the effect of poverty alleviation policies of the counties with different sustainable development levels, and the regions with higher development level have a stronger driving effect.

Development of a Real-Time Dashboard for Overdose Touchpoints: User-Centered Design Approach.

Overdose Fatality Review (OFR) is an important public health tool for shaping overdose prevention strategies in communities. However, OFR teams review only a few cases at a time, which typically represent a small fraction of the total fatalities in their jurisdiction. Such limited review could result in a partial understanding of local overdose patterns, leading to policy recommendations that do not fully address the broader community needs. This study explored the potential to enhance conventional OFRs with a data dashboard, incorporating visualizations of touchpoints-events that precede overdoses-to highlight prevention opportunities. We conducted 2 focus groups and a survey of OFR experts to characterize their information needs and design a real-time dashboard that tracks and measures decedents' past interactions with services in Indiana. Experts (N=27) were engaged, yielding insights on essential data features to incorporate and providing feedback to guide the development of visualizations. The findings highlighted the importance of showing decedents' interactions with health services (emergency medical services) and the justice system (incarcerations). Emphasis was also placed on maintaining decedent anonymity, particularly in small communities, and the need for training OFR members in data interpretation. The developed dashboard summarizes key touchpoint metrics, including prevalence, interaction frequency, and time intervals between touchpoints and overdoses, with data viewable at the county and state levels. In an initial evaluation, the dashboard was well received for its comprehensive data coverage and its potential for enhancing OFR recommendations and case selection. The Indiana touchpoints dashboard is the first to display real-time visualizations that link administrative and overdose mortality data across the state. This resource equips local health officials and OFRs with timely, quantitative, and spatiotemporal insights into overdose risk factors in their communities, facilitating data-driven interventions and policy changes. However, fully integrating the dashboard into OFR practices will likely require training teams in data interpretation and decision-making.

Food for Thought: Understanding the Impact of Food Insecurity on Fertility Dynamics in the U.S.

This paper examines the relationship between food insecurity rates and fertility rates in the United States using panel data at the county level spanning from 2010 to 2017. Drawing upon health literature that highlights the disproportionate burden of food insecurity on women and its adverse effects on maternal and child health, I investigate the hypothesis that counties with lower rates of food insecurity exhibit lower fertility rates by employing an instrumental variable regression approach with two-way fixed effects. The two most notable findings are, first, that there is weak but suggestive evidence of a positive relationship between food insecurity and fertility at the national level and second, regional disparities can cause this relationship to turn negative. These findings underscore the significance of socioeconomic factors in shaping reproductive behaviors. Further research using alternative econometric techniques is warranted to investigate causal mechanisms in the observed relationship.

Impact of institutions of higher learning on population and employment: a panel study for Germany

ABSTRACT We analyse the impact of newly established institutions of higher learning on employment by industry group and on population by age group. The study is conducted on German panel data on the county level from 2002 to 2018. During this time, numerous new institutions of higher learning were established in counties thus far not serviced. To account for the likely non-randomness of our treatment variable, the relatively small number of counties that switch from zero to at least one institution of higher learning, and the staggered nature of the individual county treatments, we utilize the newly devised generalized synthetic control method. We find that the share of manufacturing and financial services in employment rises a few years after a treatment, while the share of public sector employment declines. The level and share of young adults in the age group 18 to 35 tends to increase, in particular for rural counties. However, there is no evidence of a sustained rise in overall population or employment for counties.

Quantifying generalized trust in individuals and counties using language

Trust is predictive of civic cooperation and economic growth. Recently, the U.S. public has demonstrated increased partisan division and a surveyed decline in trust in institutions. There is a need to quantify individual and community levels of trust unobtrusively and at scale. Using observations of language across more than 16,000 Facebook users, along with their self-reported generalized trust score, we develop and evaluate a language-based assessment of generalized trust. We then apply the assessment to more than 1.6 billion geotagged tweets collected between 2009 and 2015 and derive estimates of trust across 2,041 U.S. counties. We find generalized trust was associated with more affiliative words (love, we, and friends) and less angry words (hate and stupid) but only had a weak association with social words primarily driven by strong negative associations with general othering terms (“they” and “people”). At the county level, associations with the Centers for Disease Control and Prevention (CDC) and Gallup surveys suggest that people in high-trust counties were physically healthier and more satisfied with their community and their lives. Our study demonstrates that generalized trust levels can be estimated from language as a low-cost, unobtrusive method to monitor variations in trust in large populations.

Geography, creative workforce and access to venture capital

PurposeThe purpose of this study is to investigate how proximity to metropolitan areas and local creative talent impact a company’s access to venture capital (VC). We analyze the interplay between these factors and test our hypotheses using USA county data.Design/methodology/approachThis empirical study uses multivariate regression analyses to analyze VC investment distribution across the USA at the county level between the years 1990–2011.FindingsOur findings suggest that an increase in the local creative workforce correlates with higher levels of VC funding, regardless of metro location, but has a more significant impact in metro areas, indicating the complementary nature of these factors. Furthermore, the tech industry benefits more from the local creative workforce and is less sensitive to geographic location. Our results suggest that non-metro locations with a rich local creative culture can be as effective in attracting VC as metro locations with a mediocre local creative culture. This study contributes to our understanding of the optimal geographic location for companies seeking VC.Research limitations/implicationsOne of the limitations of our research is the research timeline. Since “creative class” was not measured by the U.S. Department of Agriculture (USDA) after 2011, we cannot analyze the recent effects of creative class on VC. However, given the fact that technology-related industries increasingly dominated the VC industry in recent years, our results on tech-related industries can shed light on the future expectations of the creative class in the VC industry moving forward.Practical implicationsSome companies might find it advantageous to locate outside metro areas where the creative workforce is more abundant and accessible. Our results support this trend by demonstrating that companies must consider the tradeoff between these two factors and recognize that locating in metro areas may not always be the optimal choice for every company. A tradeoff may exist between location and the cost of accessing creative talent.Social implicationsOur results suggest that non-metro locations with a rich local creative culture can be as effective in attracting VC as metro locations with a mediocre local creative culture.Originality/valueThe existing literature emphasizes the importance of studying various factors that can help distribute VC and entrepreneurial activities across the country instead of just being concentrated in specific areas like metro regions. Although previous studies have examined broader institutional and country-level factors, local creative culture has not been considered in the context of its impact on the geographical distribution of VC. Our research highlights creative culture as a new local factor that affects VC distribution among USA counties.

Observations of enhanced rainfall variability in Kenya, East Africa

Understanding local patterns of rainfall variability is of great concern in East Africa, where agricultural productivity is dominantly rainfall dependent. However, East African rainfall climatology is influenced by numerous drivers operating at multiple scales, and local patterns of variability are not adequately understood. Here, we show evidence of substantial variability of local rainfall patterns between 1981 and 2021 at the national and county level in Kenya, East Africa. Results show anomalous patterns of both wetting and drying in both the long and short rainy seasons, with evidence of increased frequency of extreme wet and dry events through time. Observations also indicate that seasonal and intraseasonal variability increased significantly after 2013, coincident with diminished coherence between ENSO (El Nino Southern Oscillation) and rainfall. Increasing frequency and magnitude of rainfall variability suggests increasing need for local-level climate change adaptation strategies.

Spatiotemporal Analysis of Surface Urban Heat Island Dynamics in Central Yunnan City Cluster

The acceleration of urbanization has led to an increase in urban expansion and population density, exacerbating the urban heat island (UHI) effect. Moreover, the phenomenon has a significant impact on urban ecological environments and human health. Consequently, mitigating the UHI effect and enhancing the ecological environment is crucial. However previous research has primarily focused on individual cities or regional scales, with few studies analyzing all cities within urban agglomerations. This paper conducts a fine-grained spatiotemporal analysis of surface urban heat island (SUHI) effects in the Central Yunnan City Cluster from 2000 to 2021 using Landsat satellite data. We calculate the surface urban heat island intensity (SUHII) for 44 cities at the county or district level and discuss the quantitative estimation of overall SUHII changes and driving factors in the Central Yunnan City Cluster. Our findings are as follows: 1. Small cities also exhibit UHI effects, with a 75.4% probability of occurrence in the Central Yunnan City Cluster from 2000 to 2021, resulting in an overall decrease in SUHII of 1.21 °C. 2. The temperature increase rate in urban extension areas and suburban areas is faster than that in urban central areas, which is the main reason for the decreasing trend of SUHII. 3. Land use change inhibits the weakening of the SUHI effect, and population change contributes to the formation of this phenomenon. Additionally, the methods and results of this study can provide reasonable and effective insights for the future development and planning of the Central Yunnan City Cluster, thus promoting urban sustainable development.

Sarcoidosis Mortality in North Carolina: Role of Region, Race, and Other Sociodemographic Variables.

There is regional variability in sarcoidosis mortality across the United States. North Carolina ranks highly in sarcoidosis-related mortality, especially among African Americans (AA). We sought to determine any regional variability of sarcoidosis-related mortality and the relationship to sociodemographic determinants of health in North Carolina. Counties in North Carolina were categorized into three distinct geographic regions: Western, Piedmont, and Eastern. Sarcoidosis deaths were stratified by region, race, and gender. We conducted a mapping and cluster analysis utilizing ArcGIS; Global and Local Moran's I was used to determine the prevalence, spatial autocorrelation, and clustering of mortality vis-a-vis various sociodemographic variables, occupational/environmental exposures, and levels of atmospheric particulate matter less than 2.5 microns in size (PM2.5). Multivariate linear regression with exposure limited to the county level was used to determine the relationship between sarcoidosis mortality and the variables of interest. Eastern North Carolina (ENC) had the highest age-adjusted sarcoidosis mortality rate (1.16/100,000 versus 0.49/100,000 in Piedmont and 0.32/100,000 in the Western region) with statistically significant high-high mortality clusters (P < .001 for Global Moran's I). Several sociodemographic and occupational factors (proportion of AA, obese adults, and individuals working in nature) were more prevalent in ENC. Region and proportion of AA were the significant mortality predictors in our multivariate analysis. This was a cross-sectional study with exposure limited to the county level. Associations do not imply causality and risks cannot be extrapolated to the individual level. There is regional variability of sarcoidosis mortality in North Carolina. Eastern North Carolina had the highest mortality with high-high mortality clusters.

Enhancing Reliability in Automated Pavement Condition Data with a Data Quality Check Approach for Highway Agencies

Automated methods have been widely used by highway agencies to collect pavement condition data. However, there are still accuracy and precision issues associated with the reliability of the existing automated data collection methods. Therefore, this research aims to develop data quality check procedures to improve the reliability of automated pavement condition data for highway agencies. The study comprises three main components: identification of data quality check indexes; establishment of data quality thresholds; and implementation of data quality check procedures. Annual pavement rating data collected by a vendor using automated technologies and manual audit data from an independent third party were utilized to develop thresholds and test the procedures. Three districts, including two urban and one rural districts in Texas, were selected for the data quality check implementation. The results indicate that the proposed procedures, along with defined indexes and thresholds, efficiently identify pavements with data quality issues at both the section level and the county level. By pinpointing problematic areas, highway agencies can allocate resources for quick quality checks, enhancing the accuracy and precision of automated pavement condition data. The study can help highway agencies enhance the accuracy and precision of automated pavement condition data, ultimately leading to improvements in their pavement conditions.

A novel transformer-based neural network under model interpretability for improving wheat yield estimation using remotely sensed multi-variables

Timely and accurate yield prediction before wheat harvest is of great significance for food policy formulation and national economic development. Deep learning method gains importance on crop yield estimation and growth monitoring with the rapid development of deep learning models combined with remotely sensed data. However, the problems of nonlinear parameter optimization and long-term dependence in time series data have restricted the improvement of yield estimation accuracy. Transformer model completely abandons the traditional deep learning architecture and is gradually applied to time series tasks due to the advantage in modeling long-term dependence. This study introduced a novel transformer-based deep learning framework to estimate winter wheat yield in the Guanzhong Plain utilizing remotely sensed multi-variables, namely leaf area index (LAI), fraction of photosynthetically active radiation (FPAR) and vegetation temperature condition index (VTCI). The proposed model, called SSA-LSTM-transformer (SLTF), was developed for dealing with above problems under the automatic optimization capability of the sparrow search algorithm (SSA) and the long-term memory advantage of the long short-term memory (LSTM) structure. The findings demonstrated that the SLTF model achieved better estimation accuracy at the county level (R2 = 0.72, RMSE = 488.68 kg/ha) compared with the single transformer model (R2 = 0.61, RMSE = 573.99 kg/ha). The analysis on SLTF model’s performance at sampling sites over different disasters suggested that the model can effectively learn the effects of diseases, lodge and pests on crop yield estimation, which had good generalization ability at all sampling sites (R2 = 0.45, RMSE = 738.63 kg/ha). The Shapley Additive exPlanations (SHAP) approach was employed to evaluate the relative importance of remotely sensed multi-variables to yield and to understand how each input variable influences the estimated yield for global interpretability and local interpretability of the SLTF model. It was found that FPAR played the largest roles in yield estimation with the highest importance value, and FPAR and LAI from late April to late May and VTCI from late March to mid-April were regarded as important features for the estimated yield with high importance value. In conclusion, our findings indicated the potential of SLTF model for winter wheat yield estimation, which contributes to promoting further application of remotely sensed technology for agricultural production.

From bits to emissions: how FinTech benefits climate resilience?

With financial technology (FinTech) emerging as a pivotal force driving business model innovation and reshaping market competitiveness, its potential contribution to sustainability has garnered widespread attention. Drawing on carbon emissions data at the county level from 2011 to 2017 in China, alongside information on the FinTech companies, this study reveals that FinTech significantly reduces regional carbon emissions intensity. This effect is particularly pronounced in developed regions and metropolitan cities. These findings withstand rigorous scrutiny, including the application of instrumental variable strategies, controlling for financial attributes, and robustness checks altering model specifications. Mechanism analysis indicates that FinTech fosters optimization and upgrading of industrial structure and promotes the development of the ICT industry, while simultaneously driving down the proportion of coal in electricity generation and per unit GDP energy consumption, and increasing the proportion of new energy generation, thereby enhancing overall energy efficiency. The evidence presented herein supports the role of FinTech in enhancing Nationally Determined Contributions and achieving the objectives of the Paris Agreement.

Social determinants of health and treatment outcomes among a retrospective cohort of patients receiving immunotherapy for cancer.

e13744 Background: Disparities due to socioeconomic status persist in the treatment of cancer. Newer therapies, such as immune checkpoint inhibitors (ICIs), offer easier administration, lower risk of toxicity, and may improve accessibility compared to cytotoxic chemotherapy. Prior studies on ICI outcomes have shown mixed results at the county level, and there is a need to investigate social determinants of health at a more granular level. Our study aims to compare long-term ICI outcomes between socioeconomic groups at the census tract level among patients with cancer. Methods: The investigators compiled data from 2/1/2011 to 4/7/2022 on patients who received at least one dose of an ICI at a comprehensive cancer center and its outreach clinics to create a retrospective patient registry. Investigators used a secure, cloud-based REDCap registry, validated it with data quality rules, and resolved discrepancies. Clinical research specialists at Vasta Global captured most of the data. Investigators correlated patients’ ZIP codes with 2010 US Department of Agriculture Rural-Urban Commuting Area (RUCA) classifications and census tracts with 2020 Center for Disease Control Social Vulnerability Index (SVI) data. The univariate analyses used the ANOVA or Kruskal-Wallis tests, chi-square tests, and Kaplan-Meier methods. The multivariate analyses used Cox and logistic regressions, adjusting for age, race, ethnicity, type of cancer, smoking status, age-adjusted Charlson Comorbidity Index, and other comorbidities. Results: Our cohort consisted of 1,807 patients who were an average of 66 years old, predominantly male (60.8%), white (84%), non-Hispanic (98%), and had lung cancer (45.1%) as the most common tumor type. Univariate analyses found no associations between either RUCA or SVI and overall survival (OS), progression-free survival (PFS), immune-related adverse events (irAE), or time to irAE. For RUCA, multivariate analysis found that rural location of residence was associated with shorter PFS (all covariates, p 0.0386). For SVI, multivariate analysis showed no significant association with OS, PFS, number of irAEs, or time until irAE (including high-grade irAEs). Conclusions: Rural residence by ZIP code was associated with shorter PFS, indicating that there may be decreased access to ICI for these patients. However, the overall findings suggest similar ICI treatment outcomes across patients with widely variable social determinants of health by census tract. Further research is needed to understand how newer therapies can be best positioned to overcome disparities in cancer care.

Field survey data for conservation: Evaluating suitable habitat of Chinese pangolin at the county-level in eastern China (2000-2040).

The scarcity of up-to-date data on the distribution and dynamics of the Chinese pangolin (Manis pentadactyla) presented a significant challenge in developing effective conservation strategies and implementing protective measures within China. Currently, most of China's national-level nature reserves and administrative departments operate at the county level, thereby limiting the applicability of larger-scale analyses and studies for these administrative entities. This study employed 11 widely used modeling techniques created within the Biomod2 framework to predict suitable habitats for the pangolin at the county scale, while examining the correlation between environmental variables and pangolin distribution. The results revealed that highly suitable habitats in Mingxi County of China encompassed only 49 km2. Within the county-managed nature reserve, the proportion of highly suitable habitats reached as high as 52%. However, nearly half of these areas, both moderately and highly suitable habitats, remained inadequately addressed and conserved. We found nine administrative villages that necessitated prioritized conservation efforts. The study anticipated an overall expansion in suitable habitats over the ensuing two decades, with significant growth projected in the eastern regions of Xiayang and Hufang Town. This research offered a clear and applicable research paradigm for the specific administrative level at which China operates, particularly pertinent to county-level jurisdictions with established nature reserves.

The Impact of Non-Reimbursable European Funds in Romania on Gross Domestic Product

Abstract The paper represents an analysis of the access to non-reimbursable European funds in Romania, at the level of January 2024. The analysis is carried out at the territorial level, in all 41 counties in Romania, to which the Municipality of Bucharest is added. In order to determine the efficiency of the use of these funds, a correlation was made between the level of the Gross Domestic Product (GDP), as the main macroeconomic aggregate that measures the level of development and the value of the non-reimbursable European funds accessed by Romania in January 2024. The correlation shows a direct and strong link between the two variables, at each county level.

Changes in climate attributes and harvest area structures jointly determined spatial-temporal variations in water footprint of maize in the Beijing-Tianjin-Hebei region

Irrigation contributes significantly to boosting crop yield and ensuring food security. However, in the Beijing-Tianjin-Hebei (BTH) region, unsustainable irrigation practices have led to serious outcomes on freshwater resources. Balancing irrigation with crop productivity in this region, currently facing complex challenge, requires a comprehensive understanding of its spatial pattern and thus to seeking for potential optimization of current crop structures. In this study, we employed the concept of water footprint (WFP) to assess the spatial-temporal patterns of water footprint for maize in BTH region at the county level for the years 2005, 2010, 2015, and 2020, untangled the relative impacts on WFP from climate attributes and harvest area structures. Our results showed significant regional heterogeneities in both blue water requirement and green water requirement, ranging from 64.6 mm to 290.7 mm. Yearly anomalies of climate attributes and maize harvest jointly influenced water footprints, with the highest value of 1.06 × 1011 m3 occurring in the year 2015. The green water footprints, linked to precipitation, dominated the total water footprint compared to the blue water footprint associated with irrigation. Additionally, we observed an increasing influence of maize harvest area on the temporal changes in water footprints, with these changes becoming more concentrated in the east-central region over time. Our findings underscore the respective contributions of annual climate attribute changes and harvest area variations at the county level, highlighting regions where urgent interventions are required to enhance the sustainability of water usage for agriculture.

Old Empires, Modern State: Legacies of Partitions on Voting Behaviour in the 2023 Polish Parliamentary Elections

Abstract The legacies of former partitions are still visible in today’s Poland in the economy, society and politics. Our article integrates the historical and geographical perspective into the study of electoral behaviour in Poland. We conduct a geographical description of electoral results in the 2023 parliamentary elections and determine how economic and cultural attributes related to different paths of development associated with the former partitions are reflected in the apparent spatial differences. We distinguish between direct and indirect influence. It is indirect when differences in economic development (e.g. changes in agrarian structures) or cultural transformations (e.g. secularisation) resulting from former policies are reflected in different electoral choices. The influence is direct if belonging to a former empire determines electoral behaviour regardless of the economic and cultural determinants of the vote. We found that the influence concerns primarily the former Russian and Austrian regions. The analysis is conducted at the county (powiat) level.

Geospatial analysis of the distribution of uveal melanoma patients at a referral center in the United States.

e21566 Background: Approximately 2500 new uveal melanoma (UM) cases (5.2 cases per million population) are diagnosed yearly in the United States. We previously identified three unique geospatial accumulations of UM in North Carolina, Alabama, and New York based on our clinical experience. Such unexpected findings prompted us to conduct a more systematic evaluation of geospatial accumulations of UM patients at our institution. Methods: Through the electronic medical record system, we identified patients seen at our institution with a diagnosis of UM. Identified living patients and family of deceased patients were either sent an e-mail or paper letter with an internet link to a survey. The survey asked for the patient’s name, date of birth, sex, date of primary UM diagnosis, address at time of UM diagnosis and number of years at that address, and all previous addresses and dates of residence prior to the UM diagnosis. Using these addresses, ArcGIS and R software was used to analyze the geospatial distribution of our UM patients. The data was supplemented with 2020 American Community Survey census estimates at the state, county, and census tract level to estimate population-based rates of UM. Results: We identified 2718 UM patients in our clinical database, and a total of 726 participants completed the survey with a total of 2939 US addresses. The majority of addresses were precise to the street address level (77.9%). At the time of UM diagnosis, patients had lived at their current address for a median of 16 years. Pennsylvania and the neighboring states expectedly made up the majority (63.6%) of our patients, but about 1/3 of our patients resided in other states at the time of their UM diagnosis (total of 42 different states). Several counties presented elevated rates of UM with ≥5 case per 100,000 persons. We observed a group of counties in central Pennsylvania, at the border of Pennsylvania and New York, and in upper New York by the Finger Lakes with notably elevated rates of UM, which was consistent with our clinical observations at our institution. Several neighboring counties in Kentucky and Virginia also presented elevated rates. Conclusions: Through this pilot project, we have demonstrated that it is possible to collect lifetime residency information from UM patients in order to map their geospatial distribution in the US. Furthermore, although institutional bias toward referring physicians should be considered, our geospatial analysis identified accumulations of UM in areas similar to our clinical concerns. The results of our study warrant further investigations into environmental influences on UM development. Our future goal is to extend our approach through national and international collaborations to increase the number of participants in the analysis and to minimize institutional bias.

Assessment of post-pandemic economic recovery in Beijing, China, using night-time light data

ABSTRACT The coronavirus disease 2019 (COVID-19) pandemic posed a significant impact on human society, resulting in substantial changes in night-time light (NTL). This study developed a novel NTL index to estimate the quarterly Gross Domestic Product (GDP) at the county level in Beijing. This study aimed to investigate the economic recovery status in Beijing by establishing regression models of economic development before and after the pandemic. The estimated quarterly GDP at the county level was found to have a strong correlation with actual quarterly GDP statistics, with an average coefficient of determination (R 2) of 0.790. The regression models of the economic development of Beijing county-level districts before and after the pandemic were established after seasonal decomposition of the quarterly GDP. Based on the economic recovery framework for the pandemic in Beijing, most districts in the city had exhibited low economic resilience in terms of resistance and recoverability before and after the pandemic. Although the economy of Beijing is recovering gradually, it has not yet restored to pre-pandemic normal levels. These findings imply that NTL data can help assess the impact of emergency events on the disturbance of economic activity.