The impact of international air routes on tourism

We analysed the impact of the number of air routes on international tourism arrivals, using a dynamic panel data model to control for endogeneity. The analysis considers China's tourist arrivals before COVID-19, from its seventeen main source countries. The results show a significant positive effect of international air routes on arrivals. Beyond the overall effect, we differentiate long-haul and short-haul routes, and incorporate potential non-linearities. The conclusion is that air routes have a positive decreasing effect on inbound tourism demand from long-haul markets, but they are not significant for short-haul markets. Given the current post-pandemic challenges, understanding the effect of air routes on tourism demand might be incorporated into destination management strategies.

The impact of military expenditures on out of pocket healthcare payments: international evidence based on a dynamic panel data analysis

PurposeThe empirical analysis dealt in this paper emphasizes on the impact of military expenditures on out of pocket (OOP) healthcare payments. A sizeable body of defence economics literature has investigated the trade-off between military and public health expenditure, by testing the crowding-out or growth-stimulating hypothesis;does military expenditure scaling up crowd-out or promote governmental resources for social and welfare programs, including also state health financing?Design/methodology/approachIn this study, panel data from 2000 to 2018 for 129 countries is used to examine the impact of military expenditure on OOP healthcare payments. The dataset of countries is categorized into four income-groups based on World Bank's income-group classification. Dynamic panel data methodology is applied to meet study objectives.FindingsThe findings of this study indicate that military expenditure positively affects OOP payments in all the selected groups of countries, strongly supporting in this way the crowding-out hypothesis whereby increased military expenditure reduces the public financing on health. Study econometric results are robust since different and alternative changes in specifications and samples are applied in our analysis.Practical implicationsUnder the economic downturn backdrop for several economies in the previous decade and on the foreground of a potential limited governmental fiscal space related to the Covid-19 pandemic adverse economic effects, this study provides evidence that policy-makers have to adjust their government policy initiatives and prioritize Universal Health Coverage objectives. Consequently, the findings of this study reflect the necessity of governments as far as possible to moderate military expenditures and increase public financing on health in order to strengthen health care systems efficiency against households OOP spending for necessary healthcare utilization.Originality/valueDespite the fact that a sizeable body of defence economics literature has extensively examined the impact of military spending on total and public health expenditures, nevertheless to the best of our knowledge there is no empirical evidence of any direct effect of national defence spending on the main private financing component of health systems globally;the OOP healthcare payments.

Uneven domestic tourism demand in times of pandemic

This study investigates the short-run impact of the COVID-19 pandemic on the number of domestic overnight stays at the regional level in the summer season 2020. Official data for 65 regions in four countries are used for the analysis (Austria, the Czech Republic, Germany and Switzerland). Dynamic panel data models are employed to estimate a tourism demand equation (real GDP and price fluctuations) augmented by average temperatures. Estimation results reveal that domestic overnight stays evolve unevenly in the first summer after the outbreak of the COVID-19 pandemic. The short-run effects show that the number of domestic overnight stays in densely populated regions decreases by 27% in July as well as in August 2020, in comparison with the same months in previous years, ceteris paribus. To the contrary, there is a surge of 27 and 10%, respectively, for sparsely populated areas in the same months.JEL: Z3, R11 and R12.

Online food prices and shocks to product availability since Covid-19

Using web-scraping, we have gathered daily data on prices and product availability for over 50,000 unique food items from 7 major retailers in Poland since January 2020. We find a 24% drop in product availability during the first COVID-19 wave in April 2020. Next, with two-step and iterative GMM applied to a variety of specifications, we show a modest price response to stockouts, as a 10% decline in food product availability leads to an immediate 0.5% rise in prices throughout the first COVID-19 wave. During the subsequent pandemic waves, this link disappears. We also detect only a 4% drop in product availability following the onset of the Russian invasion of Ukraine in February 2022, thus we suspect that the recent price movements are linked primarily to cost and demand factors. Our evidence is indicative of retailers fearing consumer anger during unorthodox times. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

The diffusion of COVID-19 across Italian provinces: a spatial dynamic panel data approach with common factors

This study adopts a spatial dynamic panel data model with common factors and a connectivity matrix based on cross-province population flows to help explain the spread of COVID-19 infections across Italian provinces during the period 2020–21. We find that an increase in the infections in a province has a positive and statistically significant effect on neighbours' infections, which highlights the relevance of spatial spillover effects. This finding is robust to several robustness checks. Furthermore, we investigate cross-provincial transmission heterogeneity using a heterogeneous spatial dynamic panel, which provides novel insights into the diffusion patterns of the disease. © 2023 Regional Studies Association.

The Covid-19 pandemic and European trade flows: Evidence from a dynamic panel model

This paper investigates the impact of the Covid-19 pandemic on trade flows in the case of the European countries. First, an ARDL dynamic panel model is estimated using the PMG method to analyse monthly data covering the most recent period (2019M1–2021M12);then, the GMM and PCSE approaches are applied to a much longer span of quarterly data (2000Q1–2021Q4), which also includes the Global Financial Crisis (GFC) of 2007–2009, in order to compare the trade impact of two different crises. The findings based on the monthly data provide clear evidence of the significant negative effects of the Covid-19 pandemic on both exports and imports in both the short and the long run, and also suggest that digitalisation was instrumental in mitigating the impact of the crisis and speeding up the recovery. The quarterly analysis over a longer time period indicates that both the GFC and the Covid-19 pandemic had negative effects on trade but of a different magnitude. The use of digital technology enabling remote work and e-commerce are again some of the factors likely explaining why international trade fell by less and also rebounded much more quickly during the Covid-19 pandemic compared to the GFC. © 2023 The Authors. International Journal of Finance & Economics published by John Wiley & Sons Ltd.

Colonial legacies on employment: comparisons between some former Anglophone and French colonies

Purpose: In this paper, the author assesses if the effect of structural policies, macroeconomic indicators and demographic factors on employment elasticities over the period 2000–2017 can distinguish the former French colonies from the Anglophone ones. Design/methodology/approach: Using a panel of 44 countries taken from Africa and Middle East Area, elasticities are estimated in the first stage by rolling regression. Then, both static and dynamic panel models are investigated. Findings: Results suggest big difference between the former French colonies and Anglophone ones. For the French colonies, product and labor market flexibility are found to have significant and positive impact on elasticities, while for Anglophone ones, only foreign direct investment and government size are found to have significant and positive impact. Besides, all reforms and/or economic measures need to be complemented by macroeconomic policies aimed to increase economic stability. Originality/value: The results presented in this study highlight some of the factors that appear to drive the relationship between employment and some structural policies, macroeconomic indicators and demographic factors for two groups of former colonies. The paper provides policy conclusions based on these results for the two groups. This analysis may indeed help to inform future policy discussions, yet much additional work is needed to identify macroeconomic "best practices” for encouraging employment in the post-2019 covid crisis period. © 2022, Malika Neifar.

Ad hoc government payments impact on non-real estate farm debt

Purpose: The authors examined the impact of the Market Facilitation Program (MFP) and Coronavirus Food Assistance Program (CFAP) payments to United States agricultural producers on non-real estate agricultural loans. Design/methodology/approach: The authors used quarterly, state-level commercial bank data from 2016–2020 to estimate dynamic panel models. Findings: The authors found MFP and CFAP payments not associated with the percentage of non-real estate agricultural loans with payments over 90 days late. However, these payments associated with the percentage of non-real estate agricultural loans with payments between 30 and 89 days late. The available data utilized cannot consider when producers received the actual payment and what they specifically did with those funds. Originality/value: The contribution of this study is for US policymakers and agricultural lenders. The findings could be helpful in designing and implementing future ad hoc payment programs and provide an understanding of potential shortcomings of the current safety net for agricultural producers in the Farm Bill. Additionally, findings can assist agricultural lenders in predicting the impact of ad hoc payments on their distressed loan portfolios. © 2022, Charles Martinez, Christopher N. Boyer, Tun-Hsiang Yu, S. Aaron Smith and Adam Rabinowitz.

Covid-19 impact on Latin and Asian stock markets

Purpose: The purpose of this paper is to investigate the dynamic relationship between 19 pandemic and government actions, such as governmental response index and economic support packages. Design/methodology/approach: The authors use a panel dataset of 10 American and Latin countries for the period spanning from January 2020 to April 2021 to analyze the effect of government actions on stock market returns. The authors provide robust test results that improve the understanding of the impact of the pandemic on stock market indices through the break-up structure method and the new measure of Covid-19 extracted from Narayan et al. (2021) study. Findings: Empirical results show the harmful effect of the corona virus on stock prices, hence the risk adverse behavior of investors. On the other hand, the quantitative approach reveals that the positive impact of government actions is degraded during Covid-19. Originality/value: This article highlight that government actions may be effective in reducing new infections but could generate perverse economic impact through increasing uncertainty. The authors conclude that the adjustment of macroeconomic factors and the integration of financial news improve the forecasting performance of the model based on health news. © 2022, Emerald Publishing Limited.

How do economic activities spur the COVID-19 pandemic in Russia? A dynamic panel data analysis

Russia is one of the few countries in the world that has opted for almost no policy measures involving the strong suppression of economic activity in the face of the epidemic disaster brought about by the new coronavirus (COVID-19). This makes Russia a valuable subject of social experiments through which the association between economic activity and the spread of the virus can be explored. This paper presents a dynamic panel data analysis to examine the extent to which different types of economic activity contribute to the spread of COVID-19 infection using monthly and quarterly panel data of Russian regions between March 2020 and April 2021. The results strongly supported our expectation that economic activities have a greater impact on the levels of COVID-19 transmission when they involve a larger number of inhabitants or stimulate greater consumption or social activities among citizens. It was also revealed that Russian regions vary greatly in terms of the routes that link economic activity to the spread of COVID-19. These results have important policy implications for current and future epidemic control.

Overlapping Delta and Omicron Outbreaks During the COVID-19 Pandemic: Dynamic Panel Data Estimates.

BACKGROUND: The Omicron variant of SARS-CoV-2 is more transmissible than prior variants of concern (VOCs). It has caused the largest outbreaks in the pandemic, with increases in mortality and hospitalizations. Early data on the spread of Omicron were captured in countries with relatively low case counts, so it was unclear how the arrival of Omicron would impact the trajectory of the pandemic in countries already experiencing high levels of community transmission of Delta. OBJECTIVE: The objective of this study is to quantify and explain the impact of Omicron on pandemic trajectories and how they differ between countries that were or were not in a Delta outbreak at the time Omicron occurred. METHODS: We used SARS-CoV-2 surveillance and genetic sequence data to classify countries into 2 groups: those that were in a Delta outbreak (defined by at least 10 novel daily transmissions per 100,000 population) when Omicron was first sequenced in the country and those that were not. We used trend analysis, survival curves, and dynamic panel regression models to compare outbreaks in the 2 groups over the period from November 1, 2021, to February 11, 2022. We summarized the outbreaks in terms of their peak rate of SARS-CoV-2 infections and the duration of time the outbreaks took to reach the peak rate. RESULTS: Countries that were already in an outbreak with predominantly Delta lineages when Omicron arrived took longer to reach their peak rate and saw greater than a twofold increase (2.04) in the average apex of the Omicron outbreak compared to countries that were not yet in an outbreak. CONCLUSIONS: These results suggest that high community transmission of Delta at the time of the first detection of Omicron was not protective, but rather preluded larger outbreaks in those countries. Outbreak status may reflect a generally susceptible population, due to overlapping factors, including climate, policy, and individual behavior. In the absence of strong mitigation measures, arrival of a new, more transmissible variant in these countries is therefore more likely to lead to larger outbreaks. Alternately, countries with enhanced surveillance programs and incentives may be more likely to both exist in an outbreak status and detect more cases during an outbreak, resulting in a spurious relationship. Either way, these data argue against herd immunity mitigating future outbreaks with variants that have undergone significant antigenic shifts.

The Interplay Between Policy and COVID-19 Outbreaks in South Asia: Longitudinal Trend Analysis of Surveillance Data.

BACKGROUND: COVID-19 transmission rates in South Asia initially were under control when governments implemented health policies aimed at controlling the pandemic such as quarantines, travel bans, and border, business, and school closures. Governments have since relaxed public health restrictions, which resulted in significant outbreaks, shifting the global epicenter of COVID-19 to India. Ongoing systematic public health surveillance of the COVID-19 pandemic is needed to inform disease prevention policy to re-establish control over the pandemic within South Asia. OBJECTIVE: This study aimed to inform public health leaders about the state of the COVID-19 pandemic, how South Asia displays differences within and among countries and other global regions, and where immediate action is needed to control the outbreaks. METHODS: We extracted COVID-19 data spanning 62 days from public health registries and calculated traditional and enhanced surveillance metrics. We use an empirical difference equation to measure the daily number of cases in South Asia as a function of the prior number of cases, the level of testing, and weekly shifts in variables with a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. RESULTS: Traditional surveillance metrics indicate that South Asian countries have an alarming outbreak, with India leading the region with 310,310 new daily cases in accordance with the 7-day moving average. Enhanced surveillance indicates that while Pakistan and Bangladesh still have a high daily number of new COVID-19 cases (n=4819 and n=3878, respectively), their speed of new infections declined from April 12-25, 2021, from 2.28 to 2.18 and 3.15 to 2.35 daily new infections per 100,000 population, respectively, which suggests that their outbreaks are decreasing and that these countries are headed in the right direction. In contrast, India's speed of new infections per 100,000 population increased by 52% during the same period from 14.79 to 22.49 new cases per day per 100,000 population, which constitutes an increased outbreak. CONCLUSIONS: Relaxation of public health restrictions and the spread of novel variants fueled the second wave of the COVID-19 pandemic in South Asia. Public health surveillance indicates that shifts in policy and the spread of new variants correlate with a drastic expansion in the pandemic, requiring immediate action to mitigate the spread of COVID-19. Surveillance is needed to inform leaders whether policies help control the pandemic.

Ad hoc government payments impact on non-real estate farm debt

Purpose>The authors examined the impact of the Market Facilitation Program (MFP) and Coronavirus Food Assistance Program (CFAP) payments to United States agricultural producers on non-real estate agricultural loans.Design/methodology/approach>The authors used quarterly, state-level commercial bank data from 2016–2020 to estimate dynamic panel models.Findings>The authors found MFP and CFAP payments not associated with the percentage of non-real estate agricultural loans with payments over 90 days late. However, these payments associated with the percentage of non-real estate agricultural loans with payments between 30 and 89 days late. The available data utilized cannot consider when producers received the actual payment and what they specifically did with those funds.Originality/value>The contribution of this study is for US policymakers and agricultural lenders. The findings could be helpful in designing and implementing future ad hoc payment programs and provide an understanding of potential shortcomings of the current safety net for agricultural producers in the Farm Bill. Additionally, findings can assist agricultural lenders in predicting the impact of ad hoc payments on their distressed loan portfolios.

COVID-19 Surveillance Updates in US Metropolitan Areas: Dynamic Panel Data Modeling.

BACKGROUND: Despite the availability of vaccines, the US incidence of new COVID-19 cases per day nearly doubled from the beginning of July to the end of August 2021, fueled largely by the rapid spread of the Delta variant. While the "Delta wave" appears to have peaked nationally, some states and municipalities continue to see elevated numbers of new cases. Vigilant surveillance including at a metropolitan level can help identify any reignition and validate continued and strong public health policy responses in problem localities. OBJECTIVE: This surveillance report aimed to provide up-to-date information for the 25 largest US metropolitan areas about the rapidity of descent in the number of new cases following the Delta wave peak, as well as any potential reignition of the pandemic associated with declining vaccine effectiveness over time, new variants, or other factors. METHODS: COVID-19 pandemic dynamics for the 25 largest US metropolitan areas were analyzed through September 19, 2021, using novel metrics of speed, acceleration, jerk, and 7-day persistence, calculated from the observed data on the cumulative number of cases as reported by USAFacts. Statistical analysis was conducted using dynamic panel data models estimated with the Arellano-Bond regression techniques. The results are presented in tabular and graphic forms for visual interpretation. RESULTS: On average, speed in the 25 largest US metropolitan areas declined from 34 new cases per day per 100,000 population, during the week ending August 15, 2021, to 29 new cases per day per 100,000 population, during the week ending September 19, 2021. This average masks important differences across metropolitan areas. For example, Miami's speed decreased from 105 for the week ending August 15, 2021, to 40 for the week ending September 19, 2021. Los Angeles, San Francisco, Riverside, and San Diego had decreasing speed over the sample period and ended with single-digit speeds for the week ending September 19, 2021. However, Boston, Washington DC, Detroit, Minneapolis, Denver, and Charlotte all had their highest speed of the sample during the week ending September 19, 2021. These cities, as well as Houston and Baltimore, had positive acceleration for the week ending September 19, 2021. CONCLUSIONS: There is great variation in epidemiological curves across US metropolitan areas, including increasing numbers of new cases in 8 of the largest 25 metropolitan areas for the week ending September 19, 2021. These trends, including the possibility of waning vaccine effectiveness and the emergence of resistant variants, strongly indicate the need for continued surveillance and perhaps a return to more restrictive public health guidelines for some areas.

Latin America and the Caribbean SARS-CoV-2 Surveillance: Longitudinal Trend Analysis.

BACKGROUND: The COVID-19 pandemic has placed unprecedented stress on economies, food systems, and health care resources in Latin America and the Caribbean (LAC). Existing surveillance provides a proxy of the COVID-19 caseload and mortalities; however, these measures make it difficult to identify the dynamics of the pandemic and places where outbreaks are likely to occur. Moreover, existing surveillance techniques have failed to measure the dynamics of the pandemic. OBJECTIVE: This study aimed to provide additional surveillance metrics for COVID-19 transmission to track changes in the speed, acceleration, jerk, and persistence in the transmission of the pandemic more accurately than existing metrics. METHODS: Through a longitudinal trend analysis, we extracted COVID-19 data over 45 days from public health registries. We used an empirical difference equation to monitor the daily number of cases in the LAC as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. COVID-19 transmission rates were tracked for the LAC between September 30 and October 6, 2020, and between October 7 and 13, 2020. RESULTS: The LAC saw a reduction in the speed, acceleration, and jerk for the week of October 13, 2020, compared to the week of October 6, 2020, accompanied by reductions in new cases and the 7-day moving average. For the week of October 6, 2020, Belize reported the highest acceleration and jerk, at 1.7 and 1.8, respectively, which is particularly concerning, given its high mortality rate. The Bahamas also had a high acceleration at 1.5. In total, 11 countries had a positive acceleration during the week of October 6, 2020, whereas only 6 countries had a positive acceleration for the week of October 13, 2020. The TAC displayed an overall positive trend, with a speed of 10.40, acceleration of 0.27, and jerk of -0.31, all of which decreased in the subsequent week to 9.04, -0.81, and -0.03, respectively. CONCLUSIONS: Metrics such as new cases, cumulative cases, deaths, and 7-day moving averages provide a static view of the pandemic but fail to identify where and the speed at which SARS-CoV-2 infects new individuals, the rate of acceleration or deceleration of the pandemic, and weekly comparison of the rate of acceleration of the pandemic indicate impending explosive growth or control of the pandemic. Enhanced surveillance will inform policymakers and leaders in the LAC about COVID-19 outbreaks.

Surveillance of the Second Wave of COVID-19 in Europe: Longitudinal Trend Analyses.

BACKGROUND: The COVID-19 pandemic has severely impacted Europe, resulting in a high caseload and deaths that varied by country. The second wave of the COVID-19 pandemic has breached the borders of Europe. Public health surveillance is necessary to inform policy and guide leaders. OBJECTIVE: This study aimed to provide advanced surveillance metrics for COVID-19 transmission that account for weekly shifts in the pandemic, speed, acceleration, jerk, and persistence, to better understand countries at risk for explosive growth and those that are managing the pandemic effectively. METHODS: We performed a longitudinal trend analysis and extracted 62 days of COVID-19 data from public health registries. We used an empirical difference equation to measure the daily number of cases in Europe as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. RESULTS: New COVID-19 cases slightly decreased from 158,741 (week 1, January 4-10, 2021) to 152,064 (week 2, January 11-17, 2021), and cumulative cases increased from 22,507,271 (week 1) to 23,890,761 (week 2), with a weekly increase of 1,383,490 between January 10 and January 17. France, Germany, Italy, Spain, and the United Kingdom had the largest 7-day moving averages for new cases during week 1. During week 2, the 7-day moving average for France and Spain increased. From week 1 to week 2, the speed decreased (37.72 to 33.02 per 100,000), acceleration decreased (0.39 to -0.16 per 100,000), and jerk increased (-1.30 to 1.37 per 100,000). CONCLUSIONS: The United Kingdom, Spain, and Portugal, in particular, are at risk for a rapid expansion in COVID-19 transmission. An examination of the European region suggests that there was a decrease in the COVID-19 caseload between January 4 and January 17, 2021. Unfortunately, the rates of jerk, which were negative for Europe at the beginning of the month, reversed course and became positive, despite decreases in speed and acceleration. Finally, the 7-day persistence rate was higher during week 2 than during week 1. These measures indicate that the second wave of the pandemic may be subsiding, but some countries remain at risk for new outbreaks and increased transmission in the absence of rapid policy responses.

Searching for the peak: Google Trends and the first COVID-19 wave in Italy

One of the difficulties faced by policymakers during the COVID-19 outbreak in Italy was the monitoring of the virus diffusion. Due to changes in the criteria and insufficient resources to test all suspected cases, the number of ‘confirmed infected’ rapidly proved to be unreliably reported by official statistics. We explore the possibility of using information obtained from Google Trends to predict the evolution of the epidemic. Following the most recent developments on the statistical analysis of longitudinal data, we estimate a dynamic heterogeneous panel. This approach allows to takes into account the presence of common shocks and unobserved components in the error term both likely to occur in this context. We find that Google queries contain useful information to predict number patients admitted to the intensive care units, number of deaths and excess mortality in Italian regions. Copyright © 2022 Inderscience Enterprises Ltd.

Colonial legacies on employment: comparisons between some former Anglophone and French colonies

PurposeIn this paper, the author assesses if the effect of structural policies, macroeconomic indicators and demographic factors on employment elasticities over the period 2000-2017 can distinguish the former French colonies from the Anglophone ones.Design/methodology/approachUsing a panel of 44 countries taken from Africa and Middle East Area, elasticities are estimated in the first stage by rolling regression. Then, both static and dynamic panel models are investigated.FindingsResults suggest big difference between the former French colonies and Anglophone ones. For the French colonies, product and labor market flexibility are found to have significant and positive impact on elasticities, while for Anglophone ones, only foreign direct investment and government size are found to have significant and positive impact. Besides, all reforms and/or economic measures need to be complemented by macroeconomic policies aimed to increase economic stability.Originality/valueThe results presented in this study highlight some of the factors that appear to drive the relationship between employment and some structural policies, macroeconomic indicators and demographic factors for two groups of former colonies. The paper provides policy conclusions based on these results for the two groups. This analysis may indeed help to inform future policy discussions, yet much additional work is needed to identify macroeconomic "best practices" for encouraging employment in the post-2019 covid crisis period.

Searching for the peak: Google Trends and the first COVID-19 wave in Italy

One of the difficulties faced by policymakers during the COVID-19 outbreak in Italy was the monitoring of the virus diffusion. Due to changes in the criteria and insufficient resources to test all suspected cases, the number of 'confirmed infected' rapidly proved to be unreliably reported by official statistics. We explore the possibility of using information obtained from Google Trends to predict the evolution of the epidemic. Following the most recent developments on the statistical analysis of longitudinal data, we estimate a dynamic heterogeneous panel. This approach allows to takes into account the presence of common shocks and unobserved components in the error term both likely to occur in this context. We find that Google queries contain useful information to predict number patients admitted to the intensive care units, number of deaths and excess mortality in Italian regions.

An empirical study on correlation among poverty, inclusive finance, and CO2 emissions in China.

This paper explores the nonlinear relationship between poverty and CO2 emissions based on the panel data of 30 provinces in China from 2005 to 2019. In this study, the autoregressive distributed lag (ARDL) model is first used. Findings confirm that poverty has a negative impact on CO2 emissions in the short run and a positive impact in the long run, while both effects of inclusive finance on CO2 emissions are negative. In order to explore the reasons for the change in the coefficient of poverty, we introduce a moderating effect (ME) model and a dynamic panel threshold (DPT) model. The result shows that the negative effect of poverty on CO2 emissions diminishes with the moderation of inclusive finance. When inclusive finance crosses the threshold value (IFI = 0.2696), the impact of poverty on CO2 emissions will change from negative to positive gradually, which verifies the applicability of the "Poverty-CO2 Paradox" in China and provides an empirical basis for breaking the "Poverty-CO2 Paradox." Consequently, deepening poverty reduction and pushing the region's inclusive finance to the threshold level are proposed as effective ways to promote CO2 emission reduction.