• مطالعات اقتصادی مرتبط با حاملهای انرژی (فسیلی، تجدیدپذیر و برق)
Fatemeh Rastehmoghadam; Mohammad Nabi Shahiki Tash; Emad Kazemzadeh
Abstract
Air pollution has emerged as a critical concern for contemporary human societies due to its significant implications for both economic stability and public health. This research investigated the potential of renewable energy adoption to enhance air quality and mitigate mortality rates associated with ...
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Air pollution has emerged as a critical concern for contemporary human societies due to its significant implications for both economic stability and public health. This research investigated the potential of renewable energy adoption to enhance air quality and mitigate mortality rates associated with air pollution. Employing the quantile regression method, the study analyzed data spanning from 1996 to 2018 across the member nations of the Regional Comprehensive Economic Partnership. The findings indicated a statistically significant and negative correlation between renewable energy consumption and air pollution-induced mortality across all quantile ranges. Consequently, the implementation of renewable energy sources in managing air pollutants was found to contribute to a reduction in mortality and an improvement in air quality. Furthermore, increased gross domestic product was observed to correlate with decreased air pollution-related mortality, while carbon dioxide emissions and the rate of urbanization were associated with a rise in such mortality. These results underscore the potential of promoting renewable energy and managing emissions as effective strategies for bolstering public health and alleviating the adverse effects of air contamination.IntroductionRapid industrialization in recent decades has led to high levels of air pollution, causing numerous health problems. Air pollution originates from various sources, including the burning of solid fuels in homes, particulate matter from industry and transportation, and ground-level ozone. The emission of air pollutants and the resulting climate change pose a significant challenge that has garnered much attention. The overuse of fossil fuels for energy production is a primary cause of these pollutants, leading to respiratory and cardiovascular diseases, as well as global warming.To address these issues, reducing dependence on fossil fuels and adopting renewable energy sources is essential. Renewable energies not only do decrease air pollution but also help preserve the environment. Recent researches indicate that the use of renewable energy can improve human health by reducing pollutants. Economic collaborations, such as the "Regional Comprehensive Economic Partnership" (RCEP) agreement, can also be effective in reducing air pollution. RCEP member countries constitute a large portion of the global economy and population and account for a substantial share of global emissions.Given these points, the main research question of this study is:Can the consumption of renewable energy reduce the number of deaths caused by air pollution in RCEP member countries?This research also examines the impact of economic growth, urbanization rate, and carbon dioxide on deaths caused by air pollution. The main objective is to identify the effect of renewable energy consumption on air pollution-related mortality in RCEP member countries using the quantile regression method over the period from 1990 to 2018. This topic is important due to its direct impact on public health.Methods and MaterialIn this research, we employ quantile regression to investigate the conditional quantiles of the dependent variable. Introduced by Koenker and Bassett Jr. in the 1970s, quantile regression is a method in statistics and econometrics used to model and analyze the effects of variables on different quantiles of the response variable's distribution. It focuses on estimating "how much" of the lowest and highest possible values of a dependent variable are influenced by a set of independent variables, rather than just the mean.In quantile regression, a specific confidence level (τ, a value between 0 and 1) is chosen, and the estimator calculates the desired quantile for the dependent variable. For instance, setting τ to 0.05 allows the estimation of the 0.05th (lower tail) and implicitly the 0.95th (upper tail) quantiles. This method is particularly useful for modeling more complex relationships and the nonlinear impact of independent variables, serving as an alternative to mean regression (like linear regression) and aiding in the analysis of heterogeneously distributed data.Moment-based Quantile RegressionTo analyze the distributional heterogeneity across countries within a panel dataset, we utilize the moment-based quantile regression approach developed by Machado and Silva (2005, 2019). This method estimates different quantiles of the outcome distribution by accounting for unobserved effects across the distribution. Following Fouquau et al. (2021), this approach assumes that the independent variables influence the dependent variable solely through a location shift. Furthermore, it examines the conditional correlation effects of the determinants of air pollution-related mortality at various quantiles.The moment-based quantile regression model can be summarized as follows:Qit (τ∣Xit) = (αi + δiq(τ))+yit′β+Zit′γq(τ) (1)In Equation (1), αi(τ)=αi+δiq(τ) is a scalar coefficient indicating the τ-quantile fixed effects for a country. Since this coefficient is not location-invariant, the distributional impact differs from the classical fixed effect. Moreover, time-invariant characteristics captured by the distributional effect allow other variables to affect the countries under investigation in different ways (Machado & Silva, 2019).In this study, we use the quantile regression model to examine the effects of gross domestic product (GDP), renewable energy (RE), carbon dioxide (CO2) emissions, and urbanization rate (UP) on air pollution-related mortality (Y). The model is specified as:QYit (τ∣ξt , Xit) = αi+ξt+β1τCO2it+β2τ REit+β3τ GDPit+β4τ UPit(2)Where GDP, CO2, RE, and UP represent economic growth, carbon dioxide emissions, renewable energy consumption, and urbanization rate, respectively, in order to analyze their effects on mortality due to air pollution.Results and DiscussionThe purpose of tables and figures in documents is to enhance your readers’ understanding of the information presented in the document. It is much lucid and efficient if the information is communicated in tables or figures.Limit the use of borders or lines in a table to those needed for clarity. In general, use a border at the top and bottom of the table, beneath column headings, and above column spanners. You may also use a border to separate a row containing totals or other summary information from other rows in the table.Do not use vertical borders to separate data and do not use borders around every cell in a table. Use spacing between columns and rows and strict alignment to clarify relations among the elements in a table. Also, add one blank double-spaced line between the table and any text to improve the visual presentation. Note that the Table 12 presents the results of the panel quantile regression. The coefficients for each independent variable across different quantiles (10th, 25th, 50th, 75th, and 90th percentiles) can be interpreted as follows:Carbon Dioxide (lCO2): The coefficient for carbon dioxide is positive and statistically significant across all quantiles, indicating a positive relationship with mortality. The impact of carbon dioxide on mortality decreases as we move from lower to higher quantiles. For example, a 1% increase in CO2 leads to a 0.32% increase in mortality at the 10th percentile, while this effect reduces to 0.198% at the 90th percentile. This suggests that the effect of CO2 emissions on mortality is more pronounced in countries with lower levels of mortality. Overall, the findings suggest that increased carbon dioxide emissions can lead to a higher mortality rate.Gross Domestic Product (LGDP): The coefficient for GDP is negative and statistically significant across all quantiles, indicating an inverse relationship with mortality. The negative impact of GDP on mortality tends to decrease in magnitude as we move from lower to higher quantiles. This suggests that economic growth is associated with a reduction in mortality rates.Urbanization Rate (LUP): The coefficient for the urbanization rate is positive and statistically significant across all quantiles, suggesting that a higher urbanization rate is associated with increased mortality. However, the positive impact of urbanization gradually decreases across higher quantiles.Renewable Energy (LRE): The coefficient for renewable energy consumption is negative and statistically significant across all quantiles, indicating that increased consumption of renewable energy is associated with a decrease in mortality rates. The negative impact of renewable energy tends to increase in magnitude across higher quantiles.In summary, the results suggest that higher carbon dioxide emissions and urbanization rates are associated with increased mortality, while higher GDP and renewable energy consumption are associated with decreased mortality. The magnitude of these effects varies across different quantiles of the mortality distribution. Table 1. Quantile regression estimation resultsNvariables90th75th50th25th10thlCO20.1891***0.1052***0.3252***0.1029***0.5123***LGDP-0.5768***-0.6749***-0.0630***-1.1041***-1.2321***LUP0.9183**0.5175**0.2967**0.2030**0.9842**LRE-0.1932***-0.0122***-0.6902***-0.2491***-0.9181***Note that the symbols *** , ** and * indicate significance levels (1%) , (5%) and (10%), respectively. Table 2. Table Title GroupNMeanStd. DeviationStd. Error MeanPosttestFactor 1xxxx.xxx.xx.xxFactor 2xxxx.xxx.xx.xx*Here is a note on the table.ConclusionAir pollution is a complex mixture of gases and particulate matter containing organic and inorganic pollutants in the air. This pollution has serious negative effects on human health and can lead to respiratory diseases, heart conditions, and even premature death. Environmental changes and globalization play a significant role in increasing air pollution and can contribute to the spread of diseases and viruses worldwide. Improving air quality and reducing air pollution can occur through the transition to cleaner energy sources, support for renewable energies, and the reduction of pollutant production. These measures can help decrease mortality caused by air pollution.Given the importance of public health, studies on air pollution and its effects on humans are essential. Serious efforts to reduce air pollution and improve air quality can help protect public health and reduce premature mortality. In this paper, the impact of air pollution on the economy and human health has been investigated.In this research, using the quantile regression method and analyzing data from the RCEP member countries from 1996 to 2018, certain results have been obtained. The results indicate that in all quantile deciles, the use of renewable energy has had a significant and negative impact on mortality. The research findings suggest that the use of renewable energy sources can facilitate improved air quality and reduced air pollution, consequently lowering mortality due to air pollution.Furthermore, the research results show that an increase in gross domestic product can lead to a decrease in the mortality rate caused by air pollution. Increased gross domestic product improves economic and social conditions, which can, in turn, facilitate better health and a reduction in mortality rates. However, the emission of carbon dioxide and the rate of urbanization lead to an increase in the mortality rate. Carbon dioxide emissions and an increased rate of urbanization lead to serious problems in societies. Increased carbon dioxide contributes to global warming and climate change, which have negative impacts on public health. An increased rate of urbanization leads to a higher mortality rate in societies. The reasons for this include traffic and road accidents, air pollution, and reduced access to healthcare services.
