Public Health Hazards under Climate Stress: The Role of Socioeconomic Dynamics in Pakistan

Public Health Hazards under Climate Stress: The Role of Socioeconomic Dynamics in Pakistan | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Public Health Hazards under Climate Stress: The Role of Socioeconomic Dynamics in Pakistan Saira Habib, Zeeshan Raees, Nuzhat Falki This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7534029/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Climate change presents a substantial risk to public health by affecting environmental conditions, disease dynamics, and the socioeconomic frameworks that underpin well-being. In poor nations such as Pakistan, where health systems encounter structural limitations, the dual stresses of climate change and socioeconomic inequalities may intensify pre-existing health risks. This research investigates the correlation between climate change, significant socioeconomic factors, and health outcomes in Pakistan. Utilizing annual time series data from 1981 to 2021, obtained from the World Development Indicators (WDI) and NASA, the Auto Regressive Distributed Lag (ARDL) model is employed after unit root test outcomes indicating a mixed order of integration. The bound F-test validates the presence of a long-term cointegrating relationship among the variables. Long-term estimations indicate that enhanced economic conditions correlate positively with health status, implying that economic expansion and higher living standards mitigate health hazards. Conversely, markers of climate change have an adverse effect, suggesting that escalating climatic stresses heighten health risks. These findings underscore the pressing necessity for cohesive policy initiatives that concurrently bolster economic resilience and alleviate the health consequences of climate change. Strategic interventions may encompass climate-resilient health infrastructure, early warning systems for climate-induced health hazards, poverty reduction initiatives, and sustainable environmental stewardship to protect human health over the long run. JEL Codes : I15, I12, Q54, Q56 Climate change Health threats Human health Population Growth GDP Figures Figure 1 Figure 2 1. Introduction Climate change is the term used to describe how the average weather patterns around the globe are changing in the long term, which has mainly been triggered in recent decades by increased greenhouse gases (i.e. carbon dioxide and methane) which are by-products of fossil fuel emissions. These gases use heat to trap in the atmosphere to bring about global warming and thus far-reaching environmental imbalances. Being ranked the 5th climate-vulnerable country despite contributing a mere 0.88 percent of the global greenhouse gas emission levels is an indication of the severity of the problem in Pakistan. Consequences have already manifested themselves; the average increase in temperature has led to severe heatwaves, weather is becoming more erratic and more extreme (storms, floods, hurricanes and droughts), ice is melting, and this contributes to increasing sea water levels, endangering coastal settlements. Ecosystems are also strained, with a great number of species unable to survive in changing or missing environment. The effects do not only result in environmental degradation but also economic and human security (Kolstad & Johansson 2011 ). The average temperatures in Pakistan have increased by close to 0.5C since the 1960s projections show that there will be even higher increases of 1.3-1.5C by 2050. The floods of 2022 that resulted in more than 1,700 deaths, 12,000 injuries, and a loss of more than USD 40 billion in the economy are only a few examples of disasters caused by the climate. Projections are threatening in the future: until 2035–2044, about 5 million more people may be subjected to extreme river floods, and by 2070–2100, an extra 1 million people every year could deal with coastal floods. Precipitations are also getting very unbalanced as we start to see less rainfall in some areas and a more intense predictable rainfall level in other parts. This fluctuation increases the threats of drought and floods. According to the World Bank, unless decisive action is taken, severe reduced development and poverty alleviation are likely in Pakistan due to the interrelated impacts of climate change related extreme weather events, environmental degradation and air pollution that are projected to shrink GDP by 18–20 percent by 2050 1 . Climate change has become one of the most important problems of our time. A lot of people's health is affected by it, especially in developing countries like Pakistan. According to the world Climate Risk Index, this country is one of the ten most likely to be hit by disasters caused by climate change, even though it doesn't contribute much to world greenhouse gas emissions. Pakistan's ecosystems and health problems are getting worse because of rising temperatures, uncertain rain patterns, more severe weather events, and worsening air quality. Environmental change is linked to several social and economic determinants that include poverty, rural-urban inequalities, inadequate healthcare services, inadequate investment in public health services, and inequalities in the level of education attainment among students. All these determinants expose the population to disease, famine, or premature death. Climate change endangers health and the world society during the 21st century. Other risks of climate change, such as extreme weather conditions, food insecurity, climate-sensitive vector-borne, warned that climate-sensitive vector-borne diseases, and environmental pollution, have been increasing according to the World Health Organization (WHO, 2021 ). Low- and middle-income countries are more than likely to be affected by negative health outcomes because they have low health infrastructure, high poverty, and poor capacity to respond to climate change among other factors (Romanello et al., 2022 ). Without any prompt action on climate change, the IPCC ( 2021 ) projects a global temperature greater than 1.5C that was experienced prior to the industrial revolution in the coming decades only. Warm temperatures have been attributed to heat waves, drought, floods, and cyclones. Such disorders impinge health both short-term and cumulatively. South Asian and Sub-Saharan African researchers have found a related health impact as climate-sensitive diseases such as malaria, dengue, cholera, and diarrhea increase as rainfall or temperature elevates. Agricultural and food production is influenced by climate variability that brings malnutrition and child stunting in scarce regions (Myers et al., 2017 ). The other empirical studies observe the climatic variables and indicators of population health based on the national and regional data. Carleton et al. ( 2022 ) analyzed more than 100 low- and middle-income countries and concluded that higher temperatures raise mortality. Such an association was the most notable in low-air-conditioned and low-healthcare-access countries. According to the Bangladesh Demographic and Health Survey, a relationship was discovered between climate change (rainfall and temperature) and respiratory diseases among children under the age of five (Khan et al., 2020 ). In these studies, the importance of the integration of meteorological data with health and household information into how climate change influences marginal groups and health care is illustrated. The health impacts of climate change are situational both socioeconomically; the exposure and changes of adaptation are dependent on a crisis. A close association was discovered between extreme weather, particularly high mean temperatures and uncertain rainfall, and increased illness and hospital stays among low-income rural individuals, using Pakistan Social and Living Standards Measurement (PSLM) and NASA climate data in 1981–2020 (Ahmed et al., 2021). People were also shielded against health effects of climate change because of schooling and income. Better-off households enjoyed clean-water, good ventilation, and Rapid medicine. These alleviated disorders related to the climate. The climate-health nexus is further augmented by urbanization and air pollution. The concentrations of PM2.5 and PM10 and the temperature of urban heat islands are increasing in South Asian megacities Delhi, Lahore and Dhaka. Urban places, however, tend to have superior health conditions. Chen et al. ( 2018 ) in a longitudinal study conducted in China found that there is an association between short term exposure to PM2.5 and cardiovascular and respiratory hospitalization. The same is reflected in the Pakistani cities. Air quality is always one of the poorest in the world. Accordingly, the problem of climate change and uncontrolled urbanization aggravates the health condition of the population in most low-income communities. People understand that climate change causes gender vulnerability. Women and children are prone to health risks as care giving, low resources, and biomass fuel cause indoor air pollution. According to UNDP ( 2020 ), the climate policy should integrate gender to eliminate systematic gaps, as well as to foster equal health outcomes. There is growing evidence that climate is changing health yet there are gaps in data. Most of the public health systems do not have meteorological data and most of the national health information systems do not capture any data on climate sensitive diseases. Moreover, many of the studies target short-term health outcomes. The chronic effects of the combination of environmental degradation in the form of a transition to chronic diseases and changes in climate due to displacement-related mental disorders are understudied. There is an increased use of mixed methods of studying these challenges. They are qualitative in understanding community perspectives and problem-solving tactics and quantitative tools that include time-series econometrics, spatial regression, and panel regression. In South Asia, the relationships between health and climate variables have been identified through Autoregressive Distributed Lag (ARDL) to be long-term. The methods give powerful results particularly with small samples and non-stationary time series data in poor countries. The paper addresses the impact of climate change on the health issues of Pakistan as one of the ten most vulnerable countries regarding climate change (Germanwatch, 2021 ). With the assistance of environmental indicators and national household data, the study focuses on 40-year exploration of the influence of temperature, precipitation, and socioeconomic variables on health impacts of diverse populations and geographies. It does this through offering information in climate-adaptive programs of public health that enhance fairness and resilience. Socioeconomic disparities as well as environmental stressors and climate-sensitive diseases like respiratory infections, waterborne diseases, vector-borne diseases are tied by the Pakistan Social and Living Standards Measurement (PSLM) and Pakistan Demographic and Health Survey (PDHS). It is important to study the impacts of climate and socioeconomic conditions on the aspects of public health. This paper looks at this relationship based on Pakistani time series data to support policymakers to build climate resilience and health equity. 2. Literature Review A significant volume of research has investigated the socioeconomic, demographic, and environmental factors influencing health and mortality in Pakistan, focusing specifically on child and maternal health. Rabbani and Qayyum ( 2017 ) identified poverty, maternal education, healthcare accessibility, vaccination coverage, and birth spacing as critical determinants of child mortality. In Pakistan, elevated death rates above global averages and are associated with deficient healthcare facilities, inadequate maternal education, and ongoing poverty (Rabbani & Qayyum, 2017 ; Latif et al., 2019 ; Bibi, 2020). Numerous studies highlight the essential impact of women's empowerment on decreasing neonatal mortality, indicating that enhanced socioeconomic position and educational achievement for women can markedly improve child health outcomes. Naeem (2021) investigated the correlation among economic indicators, CO₂ emissions, and newborn health, revealing that GDP per capita and healthcare expenditure favorably affect infant health, whereas CO₂ emissions exert a negative impact. Ali and Şenturk ( 2021 ) identified maternal education, financial hardship, and healthcare access as predictors of under-five mortality. In contrast, Aizawa ( 2021 ) emphasized disparities in neonatal mortality across South Asia, highlighting the significance of maternal education, residential location, and access to health services. Asif et al. ( 2022 ) noted that household affluence can alleviate the detrimental impacts of unfavorable socioeconomic situations, while Sial et al. ( 2022 ) identified a direct, non-linear relationship between fossil fuel usage and infant mortality. Mortality trends in Pakistan have been associated with environmental stresses. Irfan ( 2012 ) recorded a decrease in child death rates over time, although emphasized persistent issues stemming from inadequate healthcare services. Hussain (2017) analyzed the effects of heatwaves, highlighting increased susceptibility among children and women in rural regions. Tehreem et al. ( 2020 ) discovered substantial effects of temperature on water quality and death rates, highlighting the relationship between environmental deterioration and public health. Atif (2020) underscored the necessity for enhanced healthcare systems and communication methods in response to COVID-19, concentrating on socioeconomic and demographic vulnerabilities. Climate change has become a significant catalyst for health hazards in Pakistan. Chaudhry (2018) examined historical and anticipated climate trends, assessing their effects on agriculture, water resources, and health, while emphasizing institutional and regulatory obstacles to effective adaptation. Khan et al. (2022) associated climate change with extensive social, economic, and political instability, whereas the Health Policy and Planning Report ( 2014 ) emphasized the importance of cohesive policies to tackle both climate change and conflict. Ali et al. (2019) conducted a study on climate change adaptation in Punjab, revealing significant regional inequalities in preparedness and knowledge, indicating the necessity for equitable resource distribution. Naeem et al. (2020) additionally indicated that climate change may intensify conflicts about food, water, and land, especially in susceptible agricultural areas. Energy poverty constitutes an additional facet of climate associated health hazards. Nawaz ( 2021 ) noted that restricted access to modern energy services adversely affects healthcare delivery, sanitation, and general well-being. Humayun ( 2020 ) evaluated climate effects in the Hindu Kush Himalayas, whereas Shaeen et al. ( 2022 ) reported an increase in waterborne infections attributable to climate-induced flooding and droughts, advocating for legislative measures to enhance disease prevention. The World Health Organization (2023) has underscored the health ramifications of climate change, such as heat stress, respiratory disorders, malnutrition, and vector-borne diseases, while promoting the expansion of renewable energy, early warning systems, and universal access to clean water and sanitation. Methodological advancements have been investigated. Khan ( 2020 ) presented machine learning algorithms to enhance the accuracy of mortality estimates, while observing that most previous studies concentrate on single factors or regional case analyses. Thus, the current research is deficient in thorough, multi-variable assessments that concurrently integrate climate, socioeconomic, and demographic aspects at the national scale. This gap highlights the necessity for cohesive research frameworks that can encompass the intricate linkages between climate change, socioeconomic factors, and health outcomes in Pakistan. This study investigates the interplay between critical socioeconomic indicators GDP per capita, gross savings, unemployment rate, education expenditure, and population growth and climate change variables, namely temperature and precipitation, on health outcomes in Pakistan. This study seeks to elucidate the determinants of health risks by incorporating these factors into a cohesive analytical framework, thereby providing evidence-based insights to assist policymakers and healthcare professionals in formulating targeted interventions to bolster public health resilience against climate change. 3. Methodology 3.1: Conceptual Framework 3.2. Econometric Model The objective of the present research was to determine the impact of climate change and socioeconomic variable on health status of the Pakistani people. Variable were selected based on their theoretical and empirical relevance to the health climate relationship in Pakistan supported by prior studies. The data is obtained from WDI and NASA for the period 1981-2021see Table 1 below. The present study employs the subsequent model. $$\:HSₜ\:=\:\beta\:₀\:+\:\beta\:₁AVGTₜ\:+\:\beta\:₂LEDUXₜ\:+\:\beta\:₃LGDPₜ\:+\:\beta\:₄LGSₜ\:+\:\beta\:₅PERCₜ\:+\:\beta\:₆LPOPGₜ\:+\:\beta\:₇LUNPₜ\:+\:\varepsilon\:ₜ\:$$ … …… … … … … … … … (1) HSₜ = αₒ + \(\:{\sum\:}_{i=1}^{p}{\eta\:}_{1}\:(\) HS)ₜ₋ i + \(\:{\sum\:}_{i=0}^{\eta\:}\eta\:₂\:△\) AVGTₜ₋ i + \(\:{\sum\:}_{i=0}^{\eta\:}\eta\:₃△\) LEDUXₜ₋ i + \(\:{\sum\:}_{i=0}^{\eta\:}\eta\:₄△\) LGDPₜ₋ i + \(\:{\sum\:}_{i=0}^{\eta\:}\eta\:₅△\) LGSₜ₋ i + \(\:{\sum\:}_{i=0}^{\eta\:}\eta\:₆△\) PERCₜ₋ i + \(\:{\sum\:}_{i=0}^{\eta\:}\eta\:₇△\) LPOPGₜ₋ i + \(\:{\sum\:}_{i=0}^{\eta\:}\eta\:₈△\) LUNPₜ₋ i +µₜ ………… (2) 3.3. Definition and Description of Variables. Table 1 provides a thorough description of the variables, their abbreviations, and the data source. Table:1 Definition and Sources of Data Variables. Abbreviations Data Source Description. Health status HS WDI Mortality, crude (per 1000 people), (proxy of health status) GDP GDP WDI GDP (US $ ), (Logged value) Unemployment UNP WDI Unemployment, total (% of total labor force) Govt spending on Edu EDUX WDI Government expenditure on education, total (% of GDP) Gross saving GS WDI Gross savings (US $ ), (Logged value) Population Growth POPG WDI Population growth (annual %), (Logged value) Precipitation PERC NASA Precipitation (mm/day) (Proxy of Rain) Temperature AVGT NASA Average temperature The mortality rate is a crucial indicator employed in demographic analysis and public health research to understand mortality trends and assess the impact of various factors on a population's health condition. The variable is dependent, and the data has been obtained from the World Development Indicators for the period from 1981 to 2021. Gross Domestic Product (GDP) is a macroeconomic indicator that signifies the total value of goods and services produced inside a country's borders. Unemployment is a vital economic metric, indicating job availability and individuals' capacity to obtain employment. Government spending on education as a proportion of GDP is a crucial indicator of a government's commitment to investing in human capital and ensuring access to quality education. Gross saving denotes the fraction of income retained by individuals or enterprises following the deduction of consumption expenditures and functions as an independent variable. Precipitation (mm/day) is a measure of the volume of water that falls over a specific location over a 24-hour duration. It aids scientists and researchers in measuring moisture distribution across various geographic locations, investigating climatic patterns, and evaluating the overall water availability of a place. Precipitation is considered an independent variable in the analysis, with data obtained from the NASA website for the period 1981–2021. The average temperature is an essential metric employed in meteorology, climate science, and environmental research, providing a thorough analysis of standard temperature conditions. Climate change is evaluated by average temperature and precipitation, as these elements directly influence health outcomes. Temperature denotes heat-related health concerns, whereas precipitation reflects water availability and flood-associated disease threats, both crucial for understanding the climate-health nexus in Pakistan. 3.4 Estimation Technique and ARDL Co-integration Autoregressive Distributed Lag (ARDL) is employed as a useful tool for analyzing long-term and short-term variable correlations in econometric models. It manages timeseries data with varied integration orders and is suitable for investigating the dynamic relationship between health, climate change, and socioeconomic variables. ARDL accommodates small sample sizes and accounts for endogeneity and serial correlation issues. HSₜ = ηₒ + \(\:{\sum\:}_{i=1}^{p}{\eta\:}_{1}\:(\) HS)ₜ₋ i + \(\:{\sum\:}_{i=0}^{q}\eta\:₂\:(\) AVGT)ₜ₋ i + \(\:{\sum\:}_{i=0}^{q}\eta\:₃(\) LEDUX)ₜ₋ i + \(\:{\sum\:}_{i=0}^{q}\eta\:₄\) (LGDP)ₜ₋ i + \(\:{\sum\:}_{i=0}^{q}\eta\:₅\) (LGS)ₜ₋ i + \(\:{\sum\:}_{i=0}^{q}\eta\:₆\) (PERC)ₜ₋ i + \(\:{\sum\:}_{i=0}^{q}\eta\:₇\) (LPOPG)ₜ₋ i + \(\:{\sum\:}_{i=0}^{q}\eta\:₈(\) LUNP)ₜ₋ i +λ₁(AVGT)ₜ+ λ₂(LEDUX)ₜ+ λ₃(LGDP)ₜ+ λ₄(LGS)ₜ+ λ₅(PERC)ₜ+ λ₆(LPOPG)ₜ+ λ₇(LUNP) ₜ …………………………….(3) Where in the above equation. Health status (HS) Death rate represents the level of death rate in time t. Climate change variables (average temperature and precipitation), economic variables (GDP and gross saving), socio-economic factors unemployment rate, education expenditure, and population growth are the values of the independent variables in time t. λ, η are the short-run coefficients corresponding to the respective lagged variables. ɛ represents the error term capturing the unexplained variation in the dependent variable 4. Results 4.1. Descriptive Statistics The descriptive statistics of the variables that were used in our investigation are shown in Table 2 below. Data from the Jarque-bera test, as well as the mean, median, minimum, standard deviation, kurtosis, and skewness, are included. Table 2 Descriptive Statistics LDRATE. AVGT. LEDUX. LGDPC. LGS. PERC. LPOPG. LUNP. Mean. 0.95 26.57 0.39 10.97 1.01 0.51 2.57 0.51 Median. 0.93 26.68 0.4 10.9 1.01 0.441 2.56 0.61 Maximum. 1.08 27.81 0.48 11.6 1.03 3.178 4.42 0.89 Minimum. 0.83 24.14 0.25 10.4 0.99 0.043 1.2 -0.4 Std. Dev. . 0.08 0.78 0.057 0.37 0.015 0.49 0.81 0.36 Skewness. 0.14 -0.8 -0.54 0.19 0.01 4.13 0.15 -1.4 Kurtosis. 1.5 3.71 2.87 1.68 1.66 22.99 2.26 3.78 Jarque-Bera. 3.86 5.38 1.97 3.24 3.07 798.7 1.09 13.8 Probability. 0.15 0.07 0.37 0.19 0.23 0 0.58 0.001 Source: Authors’ work Table 2 indicates descriptive statistics of the study variables. Its average values vary between 0.25 of LEDUX, 26.57 of AVGT. The greatest value is reported in the case of LPOPG (4.42) and the lowest one in the case of LUNP (-0.40). The largest values of dispersion belong to LPOPG (0.81 standard deviation), and the lowest dispersion values display LGS (0.015). The results on skew scores have shown that LDRATE, LGDPC, LGS, and LPOPG are positively skewed, in contrast to the rest AVGT, LEDUX, and PERC, and LUNP that are negatively skewed. The kurtosis values indicate that majority of the variables are neither normal nor moderately peaked, except PERC which has a high value (22.99). Jarque Bera probability outcomes indicate that all variables except PERC and LUNP are not rejecting the null hypothesis of normal distribution, which is an indication of normal distribution since no evidence in support of the alternative hypothesis has been found. 4.2. Stationarity Test In Table 3 , we can see the results of the Augmented Dickey-Fuller (ADF) unit root test. These variables include AVGT, PEC, and LEDUX, which are all stationary at level (I(0)). On the other hand, LDRATE, LUNP, LGS, and LGDP are non-stationary at level but become stationary after first differencing (I(1)). For time series data with I(0) and I(1) variables but no I(2), the Autoregressive Distributed Lag (ARDL) model is a good fit, and the combination of integration orders confirms its use. This study's variables interact in a way that the ARDL method can effectively capture because of its flexibility in lag selection, ability to estimate long-run and short-run dynamics, and effectiveness with small sample numbers. Table 3 Results of Unit root test (ADF) Variables. At Level. 1st Difference. Decision. LDRATE -1.38 -6.81*** I (1) AVGT -5.23*** -9.48*** I (0) LEDUX -3.56** -7.23*** I (0) LGDPC -0.61 -8.37*** I (1) LGS -0.99 -8.63*** I (1) PERC -4.72*** -9.33*** I (0) LPOPG -1.98 -5.45*** I (1) LUNP -2.065 -5.69*** I (1) Source: Authors’ Estimation. Notes: (*) Significant at the 10%; (**) Significant at the 5%; (***) Significant at the 1%. 4.3. F-Bound Test To ascertain whether a long-term link between variables exists and in which direction within a time series framework, a statistical technique known as the bound test is employed. The bound test analyzes the relationship between variables using t- and F-statistics at different levels. According to Table 4 , there is no degree of relationship in this instance. Table 4 Bound Test Results Test Statistic. Value. Signif. . I(0) . I(1) . F-statistic. 7.244651 10% 1.92 2.89 K. 7 5% 2.17 3.21 2.50% 2.43 3.51 1% 2.73 3.9 4.4 Empirical Results The table below, designated as number 5, depicts the short-run results of the estimation. It outlines the immediate effects of average temperature, educational expenditure, precipitation, gross savings, population growth, unemployment, and current GDP on health risks. Table 5 displays the coefficients, standard errors, t-statistics, and probabilities for each variable. T-statistics is employed to assess the significance of the variables. Precipitation is of considerable importance as the t-statistics surpass the standard threshold of 2, and the P-value is below 0.05. In contrast, the other variables exhibit a t-statistic value surpassing 2 and a p-value exceeding 0.05; hence, average temperature, population growth, gross savings, education expenditure, unemployment, and GDP are classified as non-significant. Table 5 Short Run Results Variable Coeff. t-Stat Prob. C 2.066 3.526 0.002 LDRATE(-1)* -0.24 -2.378 0.026 AVGT** 0.003 1.08 0.29 LEDUX** -0.02 -0.55 0.59 LGDP(-1) -0.099 -3.81 0.001 LGS** -0.756 -1.931 0.066 PERC(-1) 0.025 5.702 0 LPOPG(-1) -0.035 -3.707 0.001 LUNP(-1) -0.004 -1.159 0.259 D(LDRATE(-1)) -0.29 -1.839 0.079 D(LGDPC) -0.0145 -0.515 0.62 D(PERC) 0.0127 3.054 0.01 D(LPOPG) -0.028 -2.96 0.007 D(LPOPG(-1)) 0.0242 2.712 0.013 D(LUNP) 0.0074 1.389 0.178 D(LUNP(-1)) 0.0097 1.71 0.101 CointEq(-1)* -0.235 -9.37 0 Source: Authors’ Estimation, Notes: (*) Significant at the 10%; (**) Significant at the 5%; (***) Significant at the 1%. Table 6 illustrates the results of the long-term analysis. Table 6 demonstrates that AVGT and PERC positively affect HS in Pakistan, however LEDUX, LGDP, LGS, and LPOPG will significantly negatively impact Pakistan's HS. Table 6 Long Run Results Variable Coefficient Std. Error t-Statistic Prob. AVGT 0.013 0.014 0.881 0.39 LEDUX -0.069 0.134 -0.52 0.62 LGDP -0.42 0.126 -3.34 0.01 LGS -3.23 1.787 -1.81 0.085 PERC 0.11 0.049 2.19 0.04 LPOPG -0.15 0.072 -2.07 0.051 LUNP -0.018 0.017 -1.04 0.32 C 8.81 2.455 3.588 0.01 Source: Authors’ Estimation, Notes: (*) Significant at the 10%; (**) Significant at the 5%; (***) Significant at the 1%. The ARDL long-run results presented in Table 6 highlight several significant associations between socioeconomic and environmental factors and health outcomes. Average temperature (AVGT) shows a positive but statistically insignificant relationship with health, suggesting that while rising temperatures may affect human well-being, the long-run impact is not conclusive in this model. Similarly, education (LEDUX) has a negative but insignificant effect, implying that in the long run, improvements in literacy alone may not directly translate into better health outcomes without complementary factors such as healthcare access. Economic growth (LGDP) exerts a significant negative impact, indicating that despite economic expansion, the associated costs such as environmental degradation or unequal distribution of resources may undermine overall health status. Government spending (LGS) also appears with a negative sign, marginally significant, suggesting inefficiencies in the allocation of resources that may not adequately address health challenges. Conversely, per capita resource availability (PERC) shows a positive and significant relationship, indicating that better resource distribution enhances health outcomes in the long run. Population growth (LPOPG) exerts a negative effect, significant at the 10% level, suggesting that higher population pressures strain existing resources, leading to adverse health implications. Unemployment (LUNP), although negative as expected, remains statistically insignificant, reflecting that its long-term impact may be mediated through other structural factors. Finally, the constant term (C) is positive and highly significant, indicating underlying long-run dynamics in the model not captured by the explanatory variables. Overall, these results suggest that while resource allocation plays a vital role in improving health, unchecked economic growth, rising population, and inefficient government expenditure may exacerbate health vulnerabilities in the long run. 4.5 Stability Test The stability test was conducted using the cumulative sum of recursive residuals (CUSUM) and the cumulative sum of the squares of recursive residuals (CUSUMSQ). Using the Figs. 2 a and 2 b below, visual inspection experiments were conducted to evaluate the stability of our model. The predicted variables are rejected if the blue lines exceed the red lines, which are significant bounds. The model is stable, as evidenced by the fact that all blue lines in the graphs below remain within the bounds. This is also the case for all parameters at a 5% level of significance. Red lines indicate a threshold for substantial deviations from the anticipated values. Nonlinearity in the data is indicated when the blue line intersects either of the red lines. Conversely, if the plot remains within the red lines, it suggests that the data is linear, and we do not reject the estimated variables. The CUSUM and CUSUM of the square graphs are depicted in Fig. 1 . The model is deemed stable when the plot remains within the actual lines at a 5% level of significance. In this instance, the parameters are considered stable because the blue line is situated between the two red lines. Furthermore, the model's structural stability and overall quality of fit are confirmed by the fact that both the CUSUM and CUSUM of the square plots are less than the 0.05% threshold. 4.6 Discussion Our study's main goal is to look at how several variables related to climate change and socioeconomic control affect human health, namely death rates, in Pakistan. This study uses the ARDL methodology to look at how AVGT, EDUX, PERC, UNP, GS, POPG, and GDPCC affect health. Independent factors' effects on health status are further subdivided into their short- and long-term effects in the model. In near future, our data shows that Pakistan is seeing an increase in health concerns caused by rising temperatures. Extreme heat causes heat stroke and other heat-related ailments; it also causes problems with the respiratory system and the cardiovascular system; and it also helps bacteria spread. In contrast, there is no long-term causal association between temperature and health condition in Pakistan. This is because the country benefits from having four distinct seasons: spring, autumn, winter, and summer. Both the short- and long-term effects of precipitation on health status were negative, according to our study. This suggests that there is a correlation between higher levels of precipitation and heightened health hazards. One of the many variables impacting Pakistan's health statuses is precipitation. The general health and welfare of humans are profoundly affected by patterns of rainfall. Water contamination, sanitation procedure disruption, and infrastructure damage all contribute to the spread of dangerous diseases during floods. As a result of being displaced, people may have trouble getting medical treatment and may have to stay in overcrowded temporary housing if floods occur. Our findings point to a favorable relationship between gross domestic product and health status. Gains in gross domestic product, revenue, reducing poverty, and quality of life. Affordable healthcare, social welfare, and investments in public health programs, infrastructure, and services are all benefits of a high GDP. Better lives and fewer avoidable diseases are the results of sustained progress. According to the numbers, health results in Pakistan are positively correlated with investments in education. Improvements in healthcare utilization, health awareness, and literacy rates are all outcomes of increased investment in education. By addressing health disparities and promoting better lifestyles, this subsequently reduces health risks. Improved health conditions, stronger healthcare systems, and better results are all results of increased gross savings. However, the rapidly expanding population may have unintended consequences, such as higher health risks. 5 Conclusion This study empirically analyzes the effects of climatic change and socio-economic factors on human health status (mortality) in Pakistan from 1981 to 2021. This study utilized health hazards (death rate) as the dependent variable, whereas GDP, unemployment rate, education expenditure, gross savings, and population growth served as independent variables or explanatory factors. The primary aim of this empirical study is to ascertain the impact of independent variables on mortality in Pakistan throughout the designated timeframe. The stationarity qualities of the variables were assessed by unit root tests, which indicated that all variables exhibit mixed orders of stationarity. The long-run and short-run elasticity of the variables was determined using the ARDL co-integration approach. The ARDL bound test verifies that the series is interconnected in the long term. The long-run and short-run ARDL calculations indicate that precipitation and temperature adversely affect mortality in Pakistan. Health status exhibits a favorable correlation with socio-economic characteristics. Climate change being a major element in the world has been noted to have many impacts on the lives of human beings, where public health is one of them. Pakistan particularly is vulnerable to the negative impacts of changing climate because it is a developing country. Rising temperatures and changing precipitation patterns play a dominant role in the health risks within Pakistan. In Pakistan, the changes in precipitation can trigger cases of extreme rainfalls and consequent floods. Floods are known to destroy a lot of property, cause displacement and loss of life. Fatalities that are flood related are caused due to drowning, injuries, diseases that are transmitted by the water, and damage of vital facilities, including healthcare facilities. Climate change is affecting the disadvantaged population in Pakistan in a considerable way. The health impacts of climate change affect this group negatively especially because of the poverty levels, limited access, and poor infrastructure in health-related matters. In Pakistan, climate change can cause displacement and migration of population because of harsh weather conditions. Due to the accelerated rate of urbanization and population crowding in the cities, the current infrastructure and the healthcare system can be overloaded, which may lead to the high levels of health hazard. In order to address the growing health risk associated with climate change in Pakistan, there is a need to use adaption and mitigation methods. These include improving health care facilities and strengthening the early warning systems on dangerous meteorological events. Climate change being a major element in the world has been noted to have many impacts on the lives of human beings, where public health is one of them. Pakistan particularly is vulnerable to the negative impacts of changing climate because it is a developing country. Rising temperatures and changing precipitation patterns play a dominant role in the health risks within Pakistan. In Pakistan, the changes in precipitation can trigger cases of extreme rainfalls and consequent floods. Floods are known to destroy a lot of property, cause displacement and loss of life. Fatalities are caused by drowning, injuries, water-borne diseases, and damage to important infrastructures, like healthcare facilities, which result due to floods. Climate change is a huge problem to the deprived group of Pakistani citizens. Poverty, limited or no access to health services, and poor infrastructure make this population especially vulnerable to the adverse health impact of climate change. Climate change in Pakistan could result in the displacement and movement of the population as a result of unfavorable weather conditions. Urbanization and population growth in urban regions can affect current infrastructure and medical care, which has a chance of leading to high health risks. Adaption and mitigation methods are necessary to fight the rising health threats engendered by climate change in Pakistan. These include improving the healthcare infrastructure and strengthening early warning systems on harmful meteorological events. Economic factors that influence the health of Pakistan include GDP and rate of unemployment. High GDP leads to a better state of health provision, infrastructure, and disease prevention strategies and, therefore, increased health outcomes. Education investment increases the levels of health awareness-practices and healthier lifestyles leading to low health risks. Envelopment in unemployment can lead to health risk reduction because people can concentrate on self-management and physical exercise. Unemployment encourages people to find alternative suitable resources, therefore, improving socioeconomic status and access to health care. Gross saving may indirectly help overcome health issues in Pakistan because it increases investment in healthcare systems, facilitates economic growth and acts as a security against unforeseen fluctuations in the economy. An increase in educational spending in Pakistan would likely improve access to high-quality medical services, share information on how to take care of themselves regarding avoiding illness, and encourage growth in terms of socio-economic factors, thus reducing health risks. The limitations of this study must be mentioned. Lack of long-term data (all the available data covers only the period of 1981–2021) weakens the effectiveness of the research. Additionally, collection of cross-sectional data using surveys yields more reliable results as opposed to using only the secondary time series data. A comprehensive approach focused on the improvement of climate change and long-term economic concerns must be implemented to reduce health risks in Pakistan. 6 Policy Implication Pakistan may achieve substantial advancements in addressing several issues, including assessing health risks, economic variables, and climate change, which will ultimately improve the overall welfare of its population. The subsequent regulations have been suggested based on our examination of empirical evidence. To prevent population, increase, implement family planning programs and policies. Invest in job creation, green sectors, and infrastructure projects for underprivileged populations. To limit the negative effects of climate change (precipitation) on health status, it is essential to develop early warning systems and health precautions. Focus on pro-poor growth programs for improved health, living standards, and education. Concentrate on activities to expand Pakistan's green cover, green belt and new forestation projects with the goal of reducing the effects of climate change on health status and lowering health threats. Establish early warning systems and health response plans for flood-prone areas. Invest in climate change-resistant infrastructure. Implement community-focused climate adaptation programs. Declarations Ownership The author affirms that the content of this article is solely the intellectual property of the authors. All data analyses, interpretations, and conclusions are original and have not been published elsewhere. Ethical Clearance This study is based on secondary data obtained from publicly accessible and reputable sources such as the WDI and NASA databases. Since no human or animal subjects were involved, formal ethical approval was not required. However, all ethical standards regarding the use of secondary data and academic integrity have been strictly followed. Conflict of Interests: According to the authors, there are no conflicts of interest or competing financial interests related to this research. This study was conducted independently, and no external organization or individual had an influence on the design, data collection, analysis, or interpretation of the results. Consent to participate : Each of the authors testifies that he/she has contributed to the research work found in the present manuscript and have accepted the content thereof. None of the outside participants found in the authorship process any involvement that necessitated further consent. Permission to publish: The authors agree to the publication of this manuscript in Environmental Science and Pollution Research if accepted. All the authors are aware of the publication process of the final form of the manuscript and have approved the submission process. Funding Source This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author's Contribution Dr. Saira Habib : Conceptualization, methodology design, and analysis, Review, editing, and project supervision. Acknowledgements The authors sincerely thankful to the Higher Education Commission (HEC) of Pakistan for providing access to resources and data repositories that supported this research. Gratitude is also extended to colleagues and mentors who offered constructive feedback during the development of this study. Data Availability Statement The datasets used and/or analyzed during the current study are available from the following sources: • World Development Indicators (WDI): Publicly accessible at WDI Data Portal • NASA Climate Data: Available at NASA Data Portal These datasets were utilized under standard access provisions, and derived data supporting the findings are available from the corresponding author upon reasonable request. References Aizawa T (2021) Inequality of opportunity in infant mortality in South Asia: A decomposition analysis of survival data. Econ Hum Biology 43:101058. https://doi.org/10.1016/j.ehb.2021.101058 Ali A, Şenturk İ (2021) Justifying the impact of economic deprivation, maternal status and health infrastructure on under-five child mortality in Pakistan: An empirical analysis. 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Energy Econ 100:105338 Rabbani S, Qayyum A (2017) Comparative analysis of factors affecting child mortality in Pakistan. Res J Social Sci 4(2):1–17 Romanello M, Di Napoli C, Drummond P, Green C, Kennard H, Lampard P, Scamman D, Arnell N, Ayeb-Karlsson S, Ford LB, Belesova K (2022) The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels. Lancet 400(10363):1619–1654 Shaeen SK, Tharwani ZH, Bilal W, Islam Z, Essar MY (2022) Maternal mortality in Pakistan: Challenges, efforts, and recommendations. Annals Med Surg 81:104380 Sial MH, Arshed N, Amjad MA, Khan YA (2022) Nexus between fossil fuel consumption and infant mortality rate: A non-linear analysis. Environ Sci Pollut Res 29(38):58378–58387 Tehreem HS, Anser MK, Nassani AA, Abro MMQ, Zaman K (2020) Impact of average temperature, energy demand, sectoral value added, and population growth on water resource quality and mortality rate: It is time to stop waiting around. Environ Sci Pollut Res 27:37626–37644 UNDP (2020) Gender and Climate Change: Overview of Linkages and Policy Recommendations WHO (2021) Climate Change and Health: Key Facts World Health Organization (n.d.). WHO report on health and climate. Global Literature on Novel Coronavirus . https://pesquisa.bvsalud.org/global-literature-on-novel-coronavirus-2014-ncov/resource/pt/covidwho-1130082?lang=en Authors List Public Health Hazards under Climate Stress The Role of Socioeconomic Dynamics in Pakistan Saira Habib a * Zeeshan Raees a Nuzhat Falki b a* First and Corresponding author: Assistant professor at Department of Economics, Comsats University Islamabad, Islamabad, Pakistan ( [email protected] ) Student Department of Economics, Comsats University Islamabad, Pakistan: ( [email protected] ) b Assistant professor at Department of Economics Comsats University Islamabad, Islamabad, Pakistan ( [email protected] ). Footnotes Climate_Chage_in_Pakistan.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 15 Sep, 2025 Editor assigned by journal 05 Sep, 2025 First submitted to journal 04 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":97405,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7534029/v1/43a2fefe26a22aa7eb4ce7e7.jpg"},{"id":92083762,"identity":"87253e66-e499-4b51-b849-5df7e35419d2","added_by":"auto","created_at":"2025-09-24 12:32:28","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":86738,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003e(2 a, b)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e:Cumulative sum of recursive residuals. 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Introduction","content":"\u003cp\u003eClimate change is the term used to describe how the average weather patterns around the globe are changing in the long term, which has mainly been triggered in recent decades by increased greenhouse gases (i.e. carbon dioxide and methane) which are by-products of fossil fuel emissions. These gases use heat to trap in the atmosphere to bring about global warming and thus far-reaching environmental imbalances. Being ranked the 5th climate-vulnerable country despite contributing a mere 0.88 percent of the global greenhouse gas emission levels is an indication of the severity of the problem in Pakistan. Consequences have already manifested themselves; the average increase in temperature has led to severe heatwaves, weather is becoming more erratic and more extreme (storms, floods, hurricanes and droughts), ice is melting, and this contributes to increasing sea water levels, endangering coastal settlements. Ecosystems are also strained, with a great number of species unable to survive in changing or missing environment. The effects do not only result in environmental degradation but also economic and human security (Kolstad \u0026amp; Johansson \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe average temperatures in Pakistan have increased by close to 0.5C since the 1960s projections show that there will be even higher increases of 1.3-1.5C by 2050. The floods of 2022 that resulted in more than 1,700 deaths, 12,000 injuries, and a loss of more than USD 40\u0026nbsp;billion in the economy are only a few examples of disasters caused by the climate. Projections are threatening in the future: until 2035\u0026ndash;2044, about 5\u0026nbsp;million more people may be subjected to extreme river floods, and by 2070\u0026ndash;2100, an extra 1\u0026nbsp;million people every year could deal with coastal floods. Precipitations are also getting very unbalanced as we start to see less rainfall in some areas and a more intense predictable rainfall level in other parts. This fluctuation increases the threats of drought and floods. According to the World Bank, unless decisive action is taken, severe reduced development and poverty alleviation are likely in Pakistan due to the interrelated impacts of climate change related extreme weather events, environmental degradation and air pollution that are projected to shrink GDP by 18\u0026ndash;20 percent by 2050\u003csup\u003e1\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eClimate change has become one of the most important problems of our time. A lot of people's health is affected by it, especially in developing countries like Pakistan. According to the world Climate Risk Index, this country is one of the ten most likely to be hit by disasters caused by climate change, even though it doesn't contribute much to world greenhouse gas emissions. Pakistan's ecosystems and health problems are getting worse because of rising temperatures, uncertain rain patterns, more severe weather events, and worsening air quality. Environmental change is linked to several social and economic determinants that include poverty, rural-urban inequalities, inadequate healthcare services, inadequate investment in public health services, and inequalities in the level of education attainment among students. All these determinants expose the population to disease, famine, or premature death.\u003c/p\u003e\u003cp\u003eClimate change endangers health and the world society during the 21st century. Other risks of climate change, such as extreme weather conditions, food insecurity, climate-sensitive vector-borne, warned that climate-sensitive vector-borne diseases, and environmental pollution, have been increasing according to the World Health Organization (WHO, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Low- and middle-income countries are more than likely to be affected by negative health outcomes because they have low health infrastructure, high poverty, and poor capacity to respond to climate change among other factors (Romanello et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Without any prompt action on climate change, the IPCC (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) projects a global temperature greater than 1.5C that was experienced prior to the industrial revolution in the coming decades only. Warm temperatures have been attributed to heat waves, drought, floods, and cyclones. Such disorders impinge health both short-term and cumulatively.\u003c/p\u003e\u003cp\u003eSouth Asian and Sub-Saharan African researchers have found a related health impact as climate-sensitive diseases such as malaria, dengue, cholera, and diarrhea increase as rainfall or temperature elevates. Agricultural and food production is influenced by climate variability that brings malnutrition and child stunting in scarce regions (Myers et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The other empirical studies observe the climatic variables and indicators of population health based on the national and regional data. Carleton et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) analyzed more than 100 low- and middle-income countries and concluded that higher temperatures raise mortality. Such an association was the most notable in low-air-conditioned and low-healthcare-access countries. According to the Bangladesh Demographic and Health Survey, a relationship was discovered between climate change (rainfall and temperature) and respiratory diseases among children under the age of five (Khan et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In these studies, the importance of the integration of meteorological data with health and household information into how climate change influences marginal groups and health care is illustrated. The health impacts of climate change are situational both socioeconomically; the exposure and changes of adaptation are dependent on a crisis.\u003c/p\u003e\u003cp\u003eA close association was discovered between extreme weather, particularly high mean temperatures and uncertain rainfall, and increased illness and hospital stays among low-income rural individuals, using Pakistan Social and Living Standards Measurement (PSLM) and NASA climate data in 1981\u0026ndash;2020 (Ahmed et al., 2021). People were also shielded against health effects of climate change because of schooling and income. Better-off households enjoyed clean-water, good ventilation, and Rapid medicine. These alleviated disorders related to the climate. The climate-health nexus is further augmented by urbanization and air pollution. The concentrations of PM2.5 and PM10 and the temperature of urban heat islands are increasing in South Asian megacities Delhi, Lahore and Dhaka. Urban places, however, tend to have superior health conditions. Chen et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) in a longitudinal study conducted in China found that there is an association between short term exposure to PM2.5 and cardiovascular and respiratory hospitalization. The same is reflected in the Pakistani cities.\u003c/p\u003e\u003cp\u003eAir quality is always one of the poorest in the world. Accordingly, the problem of climate change and uncontrolled urbanization aggravates the health condition of the population in most low-income communities. People understand that climate change causes gender vulnerability. Women and children are prone to health risks as care giving, low resources, and biomass fuel cause indoor air pollution. According to UNDP (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the climate policy should integrate gender to eliminate systematic gaps, as well as to foster equal health outcomes. There is growing evidence that climate is changing health yet there are gaps in data. Most of the public health systems do not have meteorological data and most of the national health information systems do not capture any data on climate sensitive diseases. Moreover, many of the studies target short-term health outcomes. The chronic effects of the combination of environmental degradation in the form of a transition to chronic diseases and changes in climate due to displacement-related mental disorders are understudied. There is an increased use of mixed methods of studying these challenges.\u003c/p\u003e\u003cp\u003eThey are qualitative in understanding community perspectives and problem-solving tactics and quantitative tools that include time-series econometrics, spatial regression, and panel regression. In South Asia, the relationships between health and climate variables have been identified through Autoregressive Distributed Lag (ARDL) to be long-term. The methods give powerful results particularly with small samples and non-stationary time series data in poor countries. The paper addresses the impact of climate change on the health issues of Pakistan as one of the ten most vulnerable countries regarding climate change (Germanwatch, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). With the assistance of environmental indicators and national household data, the study focuses on 40-year exploration of the influence of temperature, precipitation, and socioeconomic variables on health impacts of diverse populations and geographies. It does this through offering information in climate-adaptive programs of public health that enhance fairness and resilience. Socioeconomic disparities as well as environmental stressors and climate-sensitive diseases like respiratory infections, waterborne diseases, vector-borne diseases are tied by the Pakistan Social and Living Standards Measurement (PSLM) and Pakistan Demographic and Health Survey (PDHS). It is important to study the impacts of climate and socioeconomic conditions on the aspects of public health. This paper looks at this relationship based on Pakistani time series data to support policymakers to build climate resilience and health equity.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eA significant volume of research has investigated the socioeconomic, demographic, and environmental factors influencing health and mortality in Pakistan, focusing specifically on child and maternal health. Rabbani and Qayyum (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) identified poverty, maternal education, healthcare accessibility, vaccination coverage, and birth spacing as critical determinants of child mortality. In Pakistan, elevated death rates above global averages and are associated with deficient healthcare facilities, inadequate maternal education, and ongoing poverty (Rabbani \u0026amp; Qayyum, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Latif et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bibi, 2020). Numerous studies highlight the essential impact of women's empowerment on decreasing neonatal mortality, indicating that enhanced socioeconomic position and educational achievement for women can markedly improve child health outcomes.\u003c/p\u003e\u003cp\u003eNaeem (2021) investigated the correlation among economic indicators, CO₂ emissions, and newborn health, revealing that GDP per capita and healthcare expenditure favorably affect infant health, whereas CO₂ emissions exert a negative impact. Ali and Şenturk (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) identified maternal education, financial hardship, and healthcare access as predictors of under-five mortality. In contrast, Aizawa (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) emphasized disparities in neonatal mortality across South Asia, highlighting the significance of maternal education, residential location, and access to health services. Asif et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) noted that household affluence can alleviate the detrimental impacts of unfavorable socioeconomic situations, while Sial et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) identified a direct, non-linear relationship between fossil fuel usage and infant mortality. Mortality trends in Pakistan have been associated with environmental stresses. Irfan (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) recorded a decrease in child death rates over time, although emphasized persistent issues stemming from inadequate healthcare services. Hussain (2017) analyzed the effects of heatwaves, highlighting increased susceptibility among children and women in rural regions.\u003c/p\u003e\u003cp\u003eTehreem et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) discovered substantial effects of temperature on water quality and death rates, highlighting the relationship between environmental deterioration and public health. Atif (2020) underscored the necessity for enhanced healthcare systems and communication methods in response to COVID-19, concentrating on socioeconomic and demographic vulnerabilities. Climate change has become a significant catalyst for health hazards in Pakistan. Chaudhry (2018) examined historical and anticipated climate trends, assessing their effects on agriculture, water resources, and health, while emphasizing institutional and regulatory obstacles to effective adaptation. Khan et al. (2022) associated climate change with extensive social, economic, and political instability, whereas the Health Policy and Planning Report (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) emphasized the importance of cohesive policies to tackle both climate change and conflict. Ali et al. (2019) conducted a study on climate change adaptation in Punjab, revealing significant regional inequalities in preparedness and knowledge, indicating the necessity for equitable resource distribution. Naeem et al. (2020) additionally indicated that climate change may intensify conflicts about food, water, and land, especially in susceptible agricultural areas. Energy poverty constitutes an additional facet of climate associated health hazards. Nawaz (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) noted that restricted access to modern energy services adversely affects healthcare delivery, sanitation, and general well-being.\u003c/p\u003e\u003cp\u003eHumayun (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) evaluated climate effects in the Hindu Kush Himalayas, whereas Shaeen et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported an increase in waterborne infections attributable to climate-induced flooding and droughts, advocating for legislative measures to enhance disease prevention. The World Health Organization (2023) has underscored the health ramifications of climate change, such as heat stress, respiratory disorders, malnutrition, and vector-borne diseases, while promoting the expansion of renewable energy, early warning systems, and universal access to clean water and sanitation. Methodological advancements have been investigated. Khan (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) presented machine learning algorithms to enhance the accuracy of mortality estimates, while observing that most previous studies concentrate on single factors or regional case analyses. Thus, the current research is deficient in thorough, multi-variable assessments that concurrently integrate climate, socioeconomic, and demographic aspects at the national scale. This gap highlights the necessity for cohesive research frameworks that can encompass the intricate linkages between climate change, socioeconomic factors, and health outcomes in Pakistan.\u003c/p\u003e\u003cp\u003eThis study investigates the interplay between critical socioeconomic indicators GDP per capita, gross savings, unemployment rate, education expenditure, and population growth and climate change variables, namely temperature and precipitation, on health outcomes in Pakistan. This study seeks to elucidate the determinants of health risks by incorporating these factors into a cohesive analytical framework, thereby providing evidence-based insights to assist policymakers and healthcare professionals in formulating targeted interventions to bolster public health resilience against climate change.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1: Conceptual Framework\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Econometric Model\u003c/h2\u003e\u003cp\u003eThe objective of the present research was to determine the impact of climate change and socioeconomic variable on health status of the Pakistani people. Variable were selected based on their theoretical and empirical relevance to the health climate relationship in Pakistan supported by prior studies. The data is obtained from WDI and NASA for the period 1981-2021see Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below. The present study employs the subsequent model.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:HSₜ\\:=\\:\\beta\\:₀\\:+\\:\\beta\\:₁AVGTₜ\\:+\\:\\beta\\:₂LEDUXₜ\\:+\\:\\beta\\:₃LGDPₜ\\:+\\:\\beta\\:₄LGSₜ\\:+\\:\\beta\\:₅PERCₜ\\:+\\:\\beta\\:₆LPOPGₜ\\:+\\:\\beta\\:₇LUNPₜ\\:+\\:\\varepsilon\\:ₜ\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u0026hellip; \u0026hellip;\u0026hellip; \u0026hellip; \u0026hellip; \u0026hellip; \u0026hellip; \u0026hellip; \u0026hellip; \u0026hellip; (1)\u003c/p\u003e\u003cp\u003e\u003cem\u003eHSₜ = αₒ +\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=1}^{p}{\\eta\\:}_{1}\\:(\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eHS)ₜ₋\u003csub\u003ei\u003c/sub\u003e +\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=0}^{\\eta\\:}\\eta\\:₂\\:△\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eAVGTₜ₋\u003csub\u003ei\u003c/sub\u003e +\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=0}^{\\eta\\:}\\eta\\:₃△\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eLEDUXₜ₋\u003csub\u003ei\u003c/sub\u003e+\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=0}^{\\eta\\:}\\eta\\:₄△\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eLGDPₜ₋\u003csub\u003ei\u003c/sub\u003e+\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=0}^{\\eta\\:}\\eta\\:₅△\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eLGSₜ₋\u003csub\u003ei\u003c/sub\u003e+\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=0}^{\\eta\\:}\\eta\\:₆△\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003ePERCₜ₋\u003csub\u003ei\u003c/sub\u003e+\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=0}^{\\eta\\:}\\eta\\:₇△\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eLPOPGₜ₋\u003csub\u003ei\u003c/sub\u003e+\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=0}^{\\eta\\:}\\eta\\:₈△\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eLUNPₜ₋\u003csub\u003ei\u003c/sub\u003e +\u0026micro;ₜ\u003c/em\u003e \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; (2)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Definition and Description of Variables.\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eprovides a thorough description of the variables, their abbreviations, and the data source. \u003cb\u003eTable:1 Definition and Sources of Data\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbbreviations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eData Source\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDescription.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMortality, crude (per 1000 people), (proxy of health status)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGDP (US\u003cspan\u003e$\u003c/span\u003e), (Logged value)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUNP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnemployment, total (% of total labor force)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovt spending on Edu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEDUX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGovernment expenditure on education, total (% of GDP)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGross saving\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGross savings (US\u003cspan\u003e$\u003c/span\u003e), (Logged value)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation Growth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePOPG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePopulation growth (annual %), (Logged value)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrecipitation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePERC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNASA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePrecipitation (mm/day) (Proxy of Rain)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAVGT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNASA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAverage temperature\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe mortality rate is a crucial indicator employed in demographic analysis and public health research to understand mortality trends and assess the impact of various factors on a population's health condition. The variable is dependent, and the data has been obtained from the World Development Indicators for the period from 1981 to 2021. Gross Domestic Product (GDP) is a macroeconomic indicator that signifies the total value of goods and services produced inside a country's borders. Unemployment is a vital economic metric, indicating job availability and individuals' capacity to obtain employment. Government spending on education as a proportion of GDP is a crucial indicator of a government's commitment to investing in human capital and ensuring access to quality education. Gross saving denotes the fraction of income retained by individuals or enterprises following the deduction of consumption expenditures and functions as an independent variable.\u003c/p\u003e\u003cp\u003ePrecipitation (mm/day) is a measure of the volume of water that falls over a specific location over a 24-hour duration. It aids scientists and researchers in measuring moisture distribution across various geographic locations, investigating climatic patterns, and evaluating the overall water availability of a place. Precipitation is considered an independent variable in the analysis, with data obtained from the NASA website for the period 1981\u0026ndash;2021. The average temperature is an essential metric employed in meteorology, climate science, and environmental research, providing a thorough analysis of standard temperature conditions. Climate change is evaluated by average temperature and precipitation, as these elements directly influence health outcomes. Temperature denotes heat-related health concerns, whereas precipitation reflects water availability and flood-associated disease threats, both crucial for understanding the climate-health nexus in Pakistan.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Estimation Technique and ARDL Co-integration\u003c/h2\u003e\u003cp\u003eAutoregressive Distributed Lag (ARDL) is employed as a useful tool for analyzing long-term and short-term variable correlations in econometric models. It manages timeseries data with varied integration orders and is suitable for investigating the dynamic relationship between health, climate change, and socioeconomic variables. ARDL accommodates small sample sizes and accounts for endogeneity and serial correlation issues.\u003c/p\u003e\u003cp\u003e\u003cem\u003eHSₜ = ηₒ +\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=1}^{p}{\\eta\\:}_{1}\\:(\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eHS)ₜ₋\u003csub\u003ei\u003c/sub\u003e +\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=0}^{q}\\eta\\:₂\\:(\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eAVGT)ₜ₋\u003csub\u003ei\u003c/sub\u003e +\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=0}^{q}\\eta\\:₃(\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eLEDUX)ₜ₋\u003csub\u003ei\u003c/sub\u003e+\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=0}^{q}\\eta\\:₄\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e(LGDP)ₜ₋\u003csub\u003ei\u003c/sub\u003e+\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=0}^{q}\\eta\\:₅\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e(LGS)ₜ₋\u003csub\u003ei\u003c/sub\u003e+\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=0}^{q}\\eta\\:₆\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e(PERC)ₜ₋\u003csub\u003ei\u003c/sub\u003e+\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=0}^{q}\\eta\\:₇\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e(LPOPG)ₜ₋\u003csub\u003ei\u003c/sub\u003e+\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=0}^{q}\\eta\\:₈(\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eLUNP)ₜ₋\u003csub\u003ei\u003c/sub\u003e +λ₁(AVGT)ₜ+ λ₂(LEDUX)ₜ+ λ₃(LGDP)ₜ+ λ₄(LGS)ₜ+ λ₅(PERC)ₜ+ λ₆(LPOPG)ₜ+ λ₇(LUNP)\u003c/em\u003eₜ \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.(3)\u003c/p\u003e\u003cp\u003eWhere in the above equation.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eHealth status (HS) Death rate represents the level of death rate in time t.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eClimate change variables (average temperature and precipitation), economic variables (GDP and gross saving), socio-economic factors unemployment rate, education expenditure, and population growth are the values of the independent variables in time t.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eλ, η are the short-run coefficients corresponding to the respective lagged variables.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eɛ represents the error term capturing the unexplained variation in the dependent variable\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Descriptive Statistics\u003c/h2\u003e\u003cp\u003eThe descriptive statistics of the variables that were used in our investigation are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below. Data from the Jarque-bera test, as well as the mean, median, minimum, standard deviation, kurtosis, and skewness, are included.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLDRATE.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAVGT.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLEDUX.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLGDPC.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLGS.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePERC.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLPOPG.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLUNP.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinimum.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStd. Dev. .\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSkewness.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-1.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKurtosis.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e22.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJarque-Bera.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e798.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e13.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProbability.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eSource: Authors\u0026rsquo; work\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e indicates descriptive statistics of the study variables. Its average values vary between 0.25 of LEDUX, 26.57 of AVGT. The greatest value is reported in the case of LPOPG (4.42) and the lowest one in the case of LUNP (-0.40). The largest values of dispersion belong to LPOPG (0.81 standard deviation), and the lowest dispersion values display LGS (0.015). The results on skew scores have shown that LDRATE, LGDPC, LGS, and LPOPG are positively skewed, in contrast to the rest AVGT, LEDUX, and PERC, and LUNP that are negatively skewed. The kurtosis values indicate that majority of the variables are neither normal nor moderately peaked, except PERC which has a high value (22.99). Jarque Bera probability outcomes indicate that all variables except PERC and LUNP are not rejecting the null hypothesis of normal distribution, which is an indication of normal distribution since no evidence in support of the alternative hypothesis has been found.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Stationarity Test\u003c/h2\u003e\u003cp\u003eIn Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, we can see the results of the Augmented Dickey-Fuller (ADF) unit root test. These variables include AVGT, PEC, and LEDUX, which are all stationary at level (I(0)). On the other hand, LDRATE, LUNP, LGS, and LGDP are non-stationary at level but become stationary after first differencing (I(1)). For time series data with I(0) and I(1) variables but no I(2), the Autoregressive Distributed Lag (ARDL) model is a good fit, and the combination of integration orders confirms its use. This study's variables interact in a way that the ARDL method can effectively capture because of its flexibility in lag selection, ability to estimate long-run and short-run dynamics, and effectiveness with small sample numbers.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of Unit root test (ADF)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAt Level.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1st Difference.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDecision.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDRATE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-6.81***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI (1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAVGT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-5.23***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-9.48***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI (0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLEDUX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-3.56**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-7.23***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI (0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGDPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-8.37***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI (1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-8.63***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI (1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePERC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-4.72***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-9.33***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI (0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLPOPG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.45***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI (1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLUNP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.69***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI (1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSource: Authors\u0026rsquo; Estimation. Notes: (*) Significant at the 10%; (**) Significant at the 5%; (***) Significant at the 1%.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.3. F-Bound Test\u003c/h2\u003e\u003cp\u003eTo ascertain whether a long-term link between variables exists and in which direction within a time series framework, a statistical technique known as the bound test is employed. The bound test analyzes the relationship between variables using t- and F-statistics at different levels. According to Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, there is no degree of relationship in this instance.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBound Test Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest Statistic.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSignif. .\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI(0) .\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eI(1) .\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF-statistic.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.244651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Empirical Results\u003c/h2\u003e\u003cp\u003eThe table below, designated as number 5, depicts the short-run results of the estimation. It outlines the immediate effects of average temperature, educational expenditure, precipitation, gross savings, population growth, unemployment, and current GDP on health risks. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e displays the coefficients, standard errors, t-statistics, and probabilities for each variable. T-statistics is employed to assess the significance of the variables. Precipitation is of considerable importance as the t-statistics surpass the standard threshold of 2, and the P-value is below 0.05. In contrast, the other variables exhibit a t-statistic value surpassing 2 and a p-value exceeding 0.05; hence, average temperature, population growth, gross savings, education expenditure, unemployment, and GDP are classified as non-significant.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eShort Run Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoeff.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003et-Stat\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProb.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDRATE(-1)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAVGT**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLEDUX**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGDP(-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGS**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePERC(-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.702\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLPOPG(-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLUNP(-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.259\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD(LDRATE(-1))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD(LGDPC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.0145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD(PERC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD(LPOPG)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD(LPOPG(-1))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD(LUNP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.178\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD(LUNP(-1))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCointEq(-1)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-9.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSource: Authors\u0026rsquo; Estimation, Notes: (*) Significant at the 10%; (**) Significant at the 5%; (***) Significant at the 1%.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the results of the long-term analysis. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e demonstrates that AVGT and PERC positively affect HS in Pakistan, however LEDUX, LGDP, LGS, and LPOPG will significantly negatively impact Pakistan's HS.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLong Run Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et-Statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eProb.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAVGT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLEDUX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-3.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.787\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePERC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLPOPG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLUNP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.588\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSource: Authors\u0026rsquo; Estimation, Notes: (*) Significant at the 10%; (**) Significant at the 5%; (***) Significant at the 1%.\u003c/p\u003e\u003cp\u003eThe ARDL long-run results presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e highlight several significant associations between socioeconomic and environmental factors and health outcomes. Average temperature (AVGT) shows a positive but statistically insignificant relationship with health, suggesting that while rising temperatures may affect human well-being, the long-run impact is not conclusive in this model. Similarly, education (LEDUX) has a negative but insignificant effect, implying that in the long run, improvements in literacy alone may not directly translate into better health outcomes without complementary factors such as healthcare access. Economic growth (LGDP) exerts a significant negative impact, indicating that despite economic expansion, the associated costs such as environmental degradation or unequal distribution of resources may undermine overall health status. Government spending (LGS) also appears with a negative sign, marginally significant, suggesting inefficiencies in the allocation of resources that may not adequately address health challenges.\u003c/p\u003e\u003cp\u003eConversely, per capita resource availability (PERC) shows a positive and significant relationship, indicating that better resource distribution enhances health outcomes in the long run. Population growth (LPOPG) exerts a negative effect, significant at the 10% level, suggesting that higher population pressures strain existing resources, leading to adverse health implications. Unemployment (LUNP), although negative as expected, remains statistically insignificant, reflecting that its long-term impact may be mediated through other structural factors. Finally, the constant term (C) is positive and highly significant, indicating underlying long-run dynamics in the model not captured by the explanatory variables.\u003c/p\u003e\u003cp\u003eOverall, these results suggest that while resource allocation plays a vital role in improving health, unchecked economic growth, rising population, and inefficient government expenditure may exacerbate health vulnerabilities in the long run.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Stability Test\u003c/h2\u003e\u003cp\u003eThe stability test was conducted using the cumulative sum of recursive residuals (CUSUM) and the cumulative sum of the squares of recursive residuals (CUSUMSQ). Using the Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb below, visual inspection experiments were conducted to evaluate the stability of our model. The predicted variables are rejected if the blue lines exceed the red lines, which are significant bounds. The model is stable, as evidenced by the fact that all blue lines in the graphs below remain within the bounds. This is also the case for all parameters at a 5% level of significance. Red lines indicate a threshold for substantial deviations from the anticipated values.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNonlinearity in the data is indicated when the blue line intersects either of the red lines. Conversely, if the plot remains within the red lines, it suggests that the data is linear, and we do not reject the estimated variables. The CUSUM and CUSUM of the square graphs are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The model is deemed stable when the plot remains within the actual lines at a 5% level of significance. In this instance, the parameters are considered stable because the blue line is situated between the two red lines. Furthermore, the model's structural stability and overall quality of fit are confirmed by the fact that both the CUSUM and CUSUM of the square plots are less than the 0.05% threshold.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.6 Discussion\u003c/h2\u003e\u003cp\u003eOur study's main goal is to look at how several variables related to climate change and socioeconomic control affect human health, namely death rates, in Pakistan. This study uses the ARDL methodology to look at how AVGT, EDUX, PERC, UNP, GS, POPG, and GDPCC affect health. Independent factors' effects on health status are further subdivided into their short- and long-term effects in the model. In near future, our data shows that Pakistan is seeing an increase in health concerns caused by rising temperatures. Extreme heat causes heat stroke and other heat-related ailments; it also causes problems with the respiratory system and the cardiovascular system; and it also helps bacteria spread. In contrast, there is no long-term causal association between temperature and health condition in Pakistan. This is because the country benefits from having four distinct seasons: spring, autumn, winter, and summer.\u003c/p\u003e\u003cp\u003eBoth the short- and long-term effects of precipitation on health status were negative, according to our study. This suggests that there is a correlation between higher levels of precipitation and heightened health hazards. One of the many variables impacting Pakistan's health statuses is precipitation. The general health and welfare of humans are profoundly affected by patterns of rainfall. Water contamination, sanitation procedure disruption, and infrastructure damage all contribute to the spread of dangerous diseases during floods. As a result of being displaced, people may have trouble getting medical treatment and may have to stay in overcrowded temporary housing if floods occur. Our findings point to a favorable relationship between gross domestic product and health status. Gains in gross domestic product, revenue, reducing poverty, and quality of life. Affordable healthcare, social welfare, and investments in public health programs, infrastructure, and services are all benefits of a high GDP. Better lives and fewer avoidable diseases are the results of sustained progress.\u003c/p\u003e\u003cp\u003eAccording to the numbers, health results in Pakistan are positively correlated with investments in education. Improvements in healthcare utilization, health awareness, and literacy rates are all outcomes of increased investment in education. By addressing health disparities and promoting better lifestyles, this subsequently reduces health risks. Improved health conditions, stronger healthcare systems, and better results are all results of increased gross savings. However, the rapidly expanding population may have unintended consequences, such as higher health risks.\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study empirically analyzes the effects of climatic change and socio-economic factors on human health status (mortality) in Pakistan from 1981 to 2021. This study utilized health hazards (death rate) as the dependent variable, whereas GDP, unemployment rate, education expenditure, gross savings, and population growth served as independent variables or explanatory factors.\u003c/p\u003e\u003cp\u003eThe primary aim of this empirical study is to ascertain the impact of independent variables on mortality in Pakistan throughout the designated timeframe. The stationarity qualities of the variables were assessed by unit root tests, which indicated that all variables exhibit mixed orders of stationarity. The long-run and short-run elasticity of the variables was determined using the ARDL co-integration approach. The ARDL bound test verifies that the series is interconnected in the long term. The long-run and short-run ARDL calculations indicate that precipitation and temperature adversely affect mortality in Pakistan. Health status exhibits a favorable correlation with socio-economic characteristics.\u003c/p\u003e\u003cp\u003eClimate change being a major element in the world has been noted to have many impacts on the lives of human beings, where public health is one of them. Pakistan particularly is vulnerable to the negative impacts of changing climate because it is a developing country. Rising temperatures and changing precipitation patterns play a dominant role in the health risks within Pakistan. In Pakistan, the changes in precipitation can trigger cases of extreme rainfalls and consequent floods. Floods are known to destroy a lot of property, cause displacement and loss of life. Fatalities that are flood related are caused due to drowning, injuries, diseases that are transmitted by the water, and damage of vital facilities, including healthcare facilities. Climate change is affecting the disadvantaged population in Pakistan in a considerable way. The health impacts of climate change affect this group negatively especially because of the poverty levels, limited access, and poor infrastructure in health-related matters.\u003c/p\u003e\u003cp\u003eIn Pakistan, climate change can cause displacement and migration of population because of harsh weather conditions. Due to the accelerated rate of urbanization and population crowding in the cities, the current infrastructure and the healthcare system can be overloaded, which may lead to the high levels of health hazard. In order to address the growing health risk associated with climate change in Pakistan, there is a need to use adaption and mitigation methods. These include improving health care facilities and strengthening the early warning systems on dangerous meteorological events. Climate change being a major element in the world has been noted to have many impacts on the lives of human beings, where public health is one of them. Pakistan particularly is vulnerable to the negative impacts of changing climate because it is a developing country. Rising temperatures and changing precipitation patterns play a dominant role in the health risks within Pakistan. In Pakistan, the changes in precipitation can trigger cases of extreme rainfalls and consequent floods. Floods are known to destroy a lot of property, cause displacement and loss of life.\u003c/p\u003e\u003cp\u003eFatalities are caused by drowning, injuries, water-borne diseases, and damage to important infrastructures, like healthcare facilities, which result due to floods. Climate change is a huge problem to the deprived group of Pakistani citizens. Poverty, limited or no access to health services, and poor infrastructure make this population especially vulnerable to the adverse health impact of climate change. Climate change in Pakistan could result in the displacement and movement of the population as a result of unfavorable weather conditions. Urbanization and population growth in urban regions can affect current infrastructure and medical care, which has a chance of leading to high health risks. Adaption and mitigation methods are necessary to fight the rising health threats engendered by climate change in Pakistan. These include improving the healthcare infrastructure and strengthening early warning systems on harmful meteorological events. Economic factors that influence the health of Pakistan include GDP and rate of unemployment. High GDP leads to a better state of health provision, infrastructure, and disease prevention strategies and, therefore, increased health outcomes. Education investment increases the levels of health awareness-practices and healthier lifestyles leading to low health risks. Envelopment in unemployment can lead to health risk reduction because people can concentrate on self-management and physical exercise. Unemployment encourages people to find alternative suitable resources, therefore, improving socioeconomic status and access to health care. Gross saving may indirectly help overcome health issues in Pakistan because it increases investment in healthcare systems, facilitates economic growth and acts as a security against unforeseen fluctuations in the economy. An increase in educational spending in Pakistan would likely improve access to high-quality medical services, share information on how to take care of themselves regarding avoiding illness, and encourage growth in terms of socio-economic factors, thus reducing health risks.\u003c/p\u003e\u003cp\u003eThe limitations of this study must be mentioned. Lack of long-term data (all the available data covers only the period of 1981\u0026ndash;2021) weakens the effectiveness of the research. Additionally, collection of cross-sectional data using surveys yields more reliable results as opposed to using only the secondary time series data. A comprehensive approach focused on the improvement of climate change and long-term economic concerns must be implemented to reduce health risks in Pakistan.\u003c/p\u003e"},{"header":"6 Policy Implication","content":"\u003cp\u003ePakistan may achieve substantial advancements in addressing several issues, including assessing health risks, economic variables, and climate change, which will ultimately improve the overall welfare of its population. The subsequent regulations have been suggested based on our examination of empirical evidence.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eTo prevent population, increase, implement family planning programs and policies. Invest in job creation, green sectors, and infrastructure projects for underprivileged populations.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTo limit the negative effects of climate change (precipitation) on health status, it is essential to develop early warning systems and health precautions.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eFocus on pro-poor growth programs for improved health, living standards, and education.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eConcentrate on activities to expand Pakistan's green cover, green belt and new forestation projects with the goal of reducing the effects of climate change on health status and lowering health threats.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eEstablish early warning systems and health response plans for flood-prone areas. Invest in climate change-resistant infrastructure. Implement community-focused climate adaptation programs.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eOwnership\u003c/h2\u003e\u003cp\u003eThe author affirms that the content of this article is solely the intellectual property of the authors. All data analyses, interpretations, and conclusions are original and have not been published elsewhere.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical Clearance\u003c/strong\u003e\u003cp\u003eThis study is based on secondary data obtained from publicly accessible and reputable sources such as the WDI and NASA databases. Since no human or animal subjects were involved, formal ethical approval was not required. However, all ethical standards regarding the use of secondary data and academic integrity have been strictly followed.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConflict of Interests:\u003c/h2\u003e\u003cp\u003eAccording to the authors, there are no conflicts of interest or competing financial interests related to this research. This study was conducted independently, and no external organization or individual had an influence on the design, data collection, analysis, or interpretation of the results.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cb\u003eConsent to participate\u003c/b\u003e:\u003c/strong\u003e\u003cp\u003eEach of the authors testifies that he/she has contributed to the research work found in the present manuscript and have accepted the content thereof. None of the outside participants found in the authorship process any involvement that necessitated further consent.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003ePermission to publish:\u003c/h2\u003e\u003cp\u003eThe authors agree to the publication of this manuscript in Environmental Science and Pollution Research if accepted. All the authors are aware of the publication process of the final form of the manuscript and have approved the submission process.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding Source\u003c/h2\u003e\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor's Contribution\u003c/h2\u003e\u003cp\u003e\u003cb\u003eDr. Saira Habib\u003c/b\u003e: Conceptualization, methodology design, and analysis, Review, editing, and project supervision.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThe authors sincerely thankful to the Higher Education Commission (HEC) of Pakistan for providing access to resources and data repositories that supported this research. Gratitude is also extended to colleagues and mentors who offered constructive feedback during the development of this study.\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the following sources:\u003c/p\u003e\u003cp\u003e\u0026bull; World Development Indicators (WDI): Publicly accessible at WDI Data Portal\u003c/p\u003e\u003cp\u003e\u0026bull; NASA Climate Data: Available at NASA Data Portal\u003c/p\u003e\u003cp\u003eThese datasets were utilized under standard access provisions, and derived data supporting the findings are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAizawa T (2021) Inequality of opportunity in infant mortality in South Asia: A decomposition analysis of survival data. 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Environ Sci Pollut Res 27:37626\u0026ndash;37644\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUNDP (2020) \u003cem\u003eGender and Climate Change: Overview of Linkages and Policy Recommendations\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWHO (2021) \u003cem\u003eClimate Change and Health: Key Facts\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization (n.d.). WHO report on health and climate. \u003cem\u003eGlobal Literature on Novel Coronavirus\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pesquisa.bvsalud.org/global-literature-on-novel-coronavirus-2014-ncov/resource/pt/covidwho-1130082?lang=en\u003c/span\u003e\u003cspan address=\"https://pesquisa.bvsalud.org/global-literature-on-novel-coronavirus-2014-ncov/resource/pt/covidwho-1130082?lang=en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAuthors List\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePublic Health Hazards under Climate Stress The Role of Socioeconomic Dynamics in Pakistan\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaira Habib \u003csup\u003ea\u003c/sup\u003e* Zeeshan Raees \u003csup\u003ea\u003c/sup\u003e Nuzhat Falki \u003csup\u003eb\u003c/sup\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u003csup\u003ea*\u003c/sup\u003e First and Corresponding author: Assistant professor at Department of Economics, Comsats University Islamabad, Islamabad, Pakistan ([email protected])\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStudent Department of Economics, Comsats University Islamabad, Pakistan: ([email protected])\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eb Assistant professor at Department of Economics Comsats University Islamabad, Islamabad, Pakistan ([email protected]).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Climate_Chage_in_Pakistan.pdf\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-economic-structures","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jecs","sideBox":"Learn more about [Journal of Economic Structures](http://journalofeconomicstructures.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jecs/default.aspx","title":"Journal of Economic Structures","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Climate change, Health threats, Human health, Population Growth, GDP","lastPublishedDoi":"10.21203/rs.3.rs-7534029/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7534029/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change presents a substantial risk to public health by affecting environmental conditions, disease dynamics, and the socioeconomic frameworks that underpin well-being. In poor nations such as Pakistan, where health systems encounter structural limitations, the dual stresses of climate change and socioeconomic inequalities may intensify pre-existing health risks. This research investigates the correlation between climate change, significant socioeconomic factors, and health outcomes in Pakistan. Utilizing annual time series data from 1981 to 2021, obtained from the World Development Indicators (WDI) and NASA, the Auto Regressive Distributed Lag (ARDL) model is employed after unit root test outcomes indicating a mixed order of integration. The bound F-test validates the presence of a long-term cointegrating relationship among the variables. Long-term estimations indicate that enhanced economic conditions correlate positively with health status, implying that economic expansion and higher living standards mitigate health hazards. Conversely, markers of climate change have an adverse effect, suggesting that escalating climatic stresses heighten health risks. These findings underscore the pressing necessity for cohesive policy initiatives that concurrently bolster economic resilience and alleviate the health consequences of climate change. Strategic interventions may encompass climate-resilient health infrastructure, early warning systems for climate-induced health hazards, poverty reduction initiatives, and sustainable environmental stewardship to protect human health over the long run.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eJEL Codes\u003c/strong\u003e\u003c/em\u003e: I15, I12, Q54, Q56\u003c/p\u003e","manuscriptTitle":"Public Health Hazards under Climate Stress: The Role of Socioeconomic Dynamics in Pakistan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-24 12:32:23","doi":"10.21203/rs.3.rs-7534029/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-09-15T22:13:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-05T15:06:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Economic Structures","date":"2025-09-04T04:44:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-economic-structures","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jecs","sideBox":"Learn more about [Journal of Economic Structures](http://journalofeconomicstructures.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jecs/default.aspx","title":"Journal of Economic Structures","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"95261d71-8315-44ad-8372-03ccd6329802","owner":[],"postedDate":"September 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-24T12:32:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-24 12:32:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7534029","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7534029","identity":"rs-7534029","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}