بررسی اثر غیر خطی تغییر ساختار اشتغال بر انتشار دی اکسید کربن در استان های ایران با استفاده از مدل پنل کوانتایل (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
تغییرات آب وهوایی ناشی از افزایش انتشار دی اکسید کربن و سایر گازهای گلخانه ای، یکی از مسائل حیاتی است که بشر با آن مواجه شده و خطرات قابل توجهی هم برای انسان و هم محیط زیست به وجود آورده است و در دهه های اخیر، این موضوع که چگونه انتشار دی اکسید کربن کاهش یابد، به یک مسئله جدی تبدیل شده است؛ به طوری که بسیاری از محققان را به مطالعه عوامل ایجادکننده و موثر بر دی اکسید کربن و کنترل آن ها سوق داده است. از عوامل موثر بر انتشار دی اکسید کربن می توان به تغییر ساختار اشتغال اشاره کرد که می تواند نقش مهمی در افزایش انتشار دی اکسید کربن از طریق افزایش فعالیت های صنعتی و رشد اقتصادی داشته باشد و کنترل آن می تواند اهمیت زیادی در کاهش میزان دی اکسید کربن منتشر شده داشته باشد. بنابراین، در مطالعه حاضر تاثیر تغییر ساختار اشتغال بر انتشار دی اکسید کربن در استان های ایران با استفاده از مدل رگرسیون پنل کوانتایل با اثرات ثابت غیرجمع پذیر که توسط پاول (2016) ارائه شده، طی بازه زمانی 1398-1388 مورد بررسی قرار گرفت. نتایج مطالعه نشان می دهد افزایش تغییر ساختار اشتغال؛ یعنی افزایش انتقال نیروی کار از بخش کشاورزی به سایر بخش های اقتصادی از جمله خدمات و صنعت، انتشار دی اکسید کربن را افزایش می دهد. علاوه بر این، به طور غیرمستقیم شاخص تغییر ساختار اشتغال،اثر مثبت و معناداری بر انتشار دی اکسید کربن در استان های ایران دارد. همچنین، رابطه N معکوس میان انتشار دی اکسید کربن و رشد اقتصادی در این مطالعه تایید شد و ضرایب به دست آمده برای نابرابری درآمد، منفی و معنادار و برای سرانه مصرف انرژی، صنعتی شدن و شهرنشینی مثبت و معنادار است.Investigating the Nonlinear Effect of Structural Labor Change on Carbon Dioxide Emissions in Iran’s Provinces Using the Panel Quantile Model
Climate change, caused by the increase in the emission of carbon dioxide and other greenhouse gases, is one of the critical issues that mankind has faced and has created significant risks for both humans and the environment. In recent decades, many researchers have studied the factors that cause and affect carbon dioxide and their control. Among the factors affecting the emission of carbon dioxide, we can mention the structural labor change, which can play an important role in increasing the emission of carbon dioxide through the increase of industrial activities and economic growth. Therefore, in the present study, the effect of structural labor change on carbon dioxide emissions in Iran’s provinces was investigated using the Quantile regression with non-additive fixed effects presented by Powell (2016). The results show that increasing labor transfer from the agricultural sector to other economic sectors, including services and industry, increases carbon dioxide emissions. Additionally, indirectly, the structural labor change index has a positive and significant effect on carbon dioxide emissions in Iran’s provinces. The study also confirmed an inverse N relationship between carbon dioxide emissions and economic growth. The coefficients obtained for income inequality are negative and significant, while those for per capita energy consumption, industrialization, and urbanization are positive and significant. IntroductionSince the early 1990s, the emission of carbon dioxide and other greenhouse gases has increased in most countries, aligning with economic growth. This has given rise to numerous challenges for humanity, inflicting detrimental effects on ecosystems across various parts of the world. The increase in carbon dioxide emissions over the past two decades has prompted researchers to delve into the factors influencing such emissions and their control. One significant factor influencing carbon dioxide emissions is the transfer of labor from the agricultural sector to other sectors. This transition is recognized as a hallmark of economic development, commonly referred to as a structural labor change in the field of development economics. Though most economic theories view the labor transfer as an indicator of socio-economic progress, this phenomenon also has disadvantages that can result in abnormal consequences affecting culture, the environment, society, and economy. Shao et al. (2021) and Yang et al. (2021) highlight it as a pivotal factor influencing carbon dioxide emissions and environmental degradation. Understanding the impact of this phenomenon on carbon dioxide emissions is crucial for formulating policies aimed at regulating the emitted carbon dioxide levels. In Iran, the transfer of labor from the agricultural sector to other economic sectors has risen, driven by diverse motives and concurrent with the expansion of urbanization and industrialization. This shift may entail numerous environmental challenges. Long-term statistics reveal that since 1956, the agricultural sector has lost its superiority, while the industrial and service sectors have experienced an increase in the number of workers. The disparity between the industry and services sectors compared to agriculture has widened (Mohinizadeh et al., 2019). However, in Iran, the impact of structural labor change on carbon dioxide emissions has not received significant scholarly attention. In this respect, the present research aimed to explore the nonlinear effects of structural labor change across 31 provinces in Iran during 2010–2020. The study first calculated the carbon dioxide emissions in each province. Subsequently, the analysis focused on the impact of structural labor change, particularly the transfer of labor from the agricultural sector to other economic sectors, on carbon dioxide emissions in the provinces. Materials and MethodsThe study adopted the experimental model proposed by Liu et al. (2019) and Yang et al. (2021), utilizing the subform presented in Equation (1). (1) Equation (1) defines the following variables: lnCO_2 represents the logarithm of carbon dioxide emissions per capita; lnGDP signifies the logarithm of real GDP; ln2GDP denotes the square of the logarithm of real GDP; ln3GDP represents the cube of the logarithm of real GDP; lnRatioagr indicates the logarithm of structural labor change; lnGini is the logarithm of income inequality; lnUrb denotes the logarithm of urbanization; lnIndst is the logarithm of industrialization; and lnEC stands for the logarithm of energy consumption. Furthermore, lnRatioagr×GDP represents the logarithm of the interaction term between structural labor change and real GDP. This variable was incorporated into the model due to the indirect impact of structural labor change on carbon dioxide emissions. In addition to the variable of structural labor change, the study examined the effect of other explanatory variables on carbon dioxide emissions. These variables are summarized in Table 1. Table 1. Introduction of explanatory variablesSourceDescriptionVariableStatistical Center of IranThe ratio represents the percentage of the working labor force in the agricultural sector compared to the total working population. A higher percentage indicates less change in the employment structure, while a lower percentage signifies more pronounced structural changes in the labor force.Structural labor changeEnergy balanceTotal energy consumption per capita, encompassing natural gas, kerosene, fuel oil, and gasoline (thousand liters).Energy consumptionStatistical Center of IranThe ratio of the added value of the industrial sector to the GDP (million rials)IndustrializationMinistry of Economic Affairs and FinanceReal GDP (million rials).Economic growthStatistical Center of IranGini coefficient of total consumption expenditure of urban and rural households in each province, weighted by population (percentage)Income inequalityStatistical Center of IranThe ratio of the urban population in each province to the total population of the province (percentage)Urbanization Results and DiscussionFocusing on the transfer of labor from rural and agricultural areas to urban and industrial or service centers, the present study investigated the impact of this labor transfer on carbon dioxide emissions across 31 provinces in Iran during 2010–2020. First, the carbon dioxide emissions for each province were calculated. Then, the study introduced a model based on quantile regression with nonadditive fixed effects at varying quantile levels. The primary rationale behind employing this regression technique was to offer a detailed and comprehensive analysis of the model’s response variable. This approach allows for intervention not only at the center of gravity of data but also at all levels of the distribution particularly the extremes avoiding the issues associated with assumptions such as ordinary regression, heterogeneity of variance, and the potential impact of outlier data on coefficient estimations. Consequently, the panel quantiles were used to estimate the regression model, and the results are presented in Tables 2 and 3.Table Table 2. Estimation results ation resultsvariables / (τ)5040302010 -48.59***-30.69***29.14***-24.32***-24.46*** 3.13***1.94***1.84***1.52***1.56*** -0.067***-0.041***-0.039***-0.032***-0.033*** -0.622***-0.592***-0.508***-0.758***-0.525*** -0.161-0.068***-0.120***-0.117***-0.202*** 0.0520.722***0.996***1.089***0.918*** 0.143***0.123***0.096***0.103***0.076*** 0.614***0.684***0.646***0.662***0.719*** 0.038***0.046***0.044***0.059***0.042***Source: Research resultsTble Table 3. Estimation results ation resultsvariables / (τ)90807060 -154.60***-73.48***-41.63***-46.65***ln2GDP9.99***4.73***2.96***30.9*** -0.214***-0.101***-0.058***-0.068***Ratioagri-1.99**-0.221***0.0070.612 -0.144-0.257***-0.017-0.046***lnUrb0.340***0.0360.184***0.396*** 0.106***0.130***0.135***0.128*** 0.645***0.724***0.671***0.586*** 0.126**0.015**0.0007-0.039Note: ***, ** and * represent the significance level of 1, 5 and 10%, respectively.Source: Research resultsIncreasing the proportion of the working population in the agricultural sector relative to other sectors or minimizing changes in the labor structure, except between the 60th and 70th percentiles, leads to a reduction in carbon dioxide emissions. As a result, the structural labor change exerts a direct and significant impact on the levels of carbon dioxide emissions across Iran’s provinces. As changes in the labor structure intensify, the agricultural sector might resort to machinery to compensate for the workforce reduction, maintaining production and moving towards capitalization that, in turn, amplify energy consumption and carbon dioxide emissions. Furthermore, the transition from rural areas and agricultural hubs to urban and industrial centers can increase income, thereby contributing to an increase in carbon dioxide emissions. The study also examined the indirect impact of structural labor change on carbon dioxide emissions through the economic growth channel. According to the estimation results, the coefficient for the interaction term of structural labor change and economic growth is positive and statistically significant in all quantiles, except the 60th and 70th percentiles. As noted by Yang et al. (2021), the increased transfer of labor from the agricultural sector to other sectors, particularly industry, during the course of economic development can indirectly boost economic growth and carbon dioxide emissions. The labor transfer increases as the scale and GDP rise, and there is an expansion in fossil fuel consumption accompanying economic growth, leading to a subsequent increase in carbon dioxide emissions in the provinces of Iran. The study validated two direct and indirect effects of structural labor change on carbon dioxide emissions in Iran’s provinces. In both scenarios, structural labor change contributed to an increase in carbon dioxide emissions. The first effect stems from the increasing use of machinery to compensate for the labor force depleted from the agricultural sector, leading to increased energy consumption and subsequent carbon dioxide emissions. The second effect can be explained with an eye to the increased economic growth and GDP resulting from the structural labor change, as discussed in the Lewis model. ConclusionThe study examined both the direct and indirect effects of structural labor change, in conjunction with other socio-economic variables, using a nonlinear method. The data was gathered from 31 provinces of Iran spanning from 2010–2020, and the study used a quantile regression with nonadditive fixed effects. The variable denoting labor transfer from the agricultural sector to other sectors was used as the ratio of the working population in the agricultural sector to the total working population, serving as the index for structural labor change. The findings revealed that structural labor change has a direct effect on carbon dioxide emissions. Furthermore, concerning indirect effects, it can be affirmed that the index has a positive and significant effect on the dependent variable through the indirect channel of economic growth. Considering the positive effect of labor transfer and its negative impact on carbon dioxide emissions and environmental degradation, it is recommended to adopt measures to control and regulate the labor transfer. Specifically, strategies should be devised to increase the income of workers in the agricultural sector, aiming to establish an equitable wage balance relative to other sectors. Moreover, provincial authorities should prioritize initiatives that increase the real added value in agriculture, with a focus on expanding industries associated with agricultural production, such as transformative and complementary sectors.
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