https://orcid.org/0000-0001-8731-4042
Figures
Abstract
A nation’s ability to maintain a lower level of environmental degradation is considered pivotal for achieving a robust living standard. This study evaluates the short- and long-term effects of Bangladesh’s GDP, energy consumption (ENC), food production index (FPI), and life expectancy at birth (LEB) on CO2 emission using time series data over the period 1990–2021. In doing so, the study uses an Autoregressive Distributed Lag (ARDL) bounds testing model. The short-run disequilibrium behavior of the variables is also captured using the Error Correction Model (ECM). Then, the Granger causality test was applied to identify the causal relationship between variables. The outcome reveals a long-term relationship between variables. While ENC has a significant positive impact on CO2 emissions per capita, GDP per capita exhibits a significant negative impact. Additionally, if there is any departure from equilibrium, the rate of return to equilibrium is about 67.30%. The study also found a unidirectional causal relationship between CO2 emission per capita to GDP per capita and the bidirectional causal relationship between CO2 emission per capita and FPI. Building upon the obtained results, future efforts to promote living standards can be better achieved by matching the most suitable factors for their effective response to the environment. Therefore, the study suggests that the government should promote alternative energy sources like renewable energy, carbon pricing, energy-efficient technology, eco-friendly agricultural practices, higher economic growth, and life expectancy to reduce environmental deterioration and promote living standards simultaneously.
Citation: Pramanik MNH, Hasnain MN (2025) Exploring the nexus between environmental degradation and living standard in Bangladesh: Evidence from ARDL and ECM technique. PLOS Clim 4(5): e0000421. https://doi.org/10.1371/journal.pclm.0000421
Editor: Stephane Goutte, COMUE Universite Paris-Saclay, FRANCE
Received: April 8, 2024; Accepted: April 7, 2025; Published: May 9, 2025
Copyright: © 2025 Pramanik, Hasnain. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All data can be found in the manuscript and supporting information files.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
1 Introduction
Environmental degradation is a global challenge resulting from human activities that aim to achieve a higher standard of living. Living standards measuring a population’s quality of life include income, health, education, and happiness [1]. Improved living standards often correspond to increased resource consumption, higher energy demands, industrialization, urbanization, and increased production and consumption patterns. On the one hand, countries worldwide are trying to improve people’s living standards. On the other hand, environments are deteriorating due to these activities. However, this is not true for all countries. This phenomenon aligns with the theoretical Kuznets curve, which posits that environmental degradation initially worsens with economic growth but eventually improves as societies become wealthier and can invest in cleaner technologies. That’s why the relationship between environmental degradation and living standard has become a prominent issue of argument, especially for developing countries like Bangladesh, garnering increasing attention from the academic community [2].
Bangladesh, a South Asian country, is grappling with environmental degradation due to rapid population growth, economic growth, urbanization, industrialization, and agricultural intensification, resulting in environmental pollution, soil erosion, and global warming, affecting its natural resources [3]. Rapid industrialization and urbanization in Bangladesh, aiming to become a developed country by 2041, have raised concerns about environmental deprivation and its potential negative impacts. It leads to a range of adverse effects, including diminishing soil fertility, increased health risks, and a decline in the quality of life for many people [4]. Moreover, agricultural intensification, driven by the need to boost food production and GDP, has led to soil degradation, water pollution, and deforestation. These measures have reduced biodiversity and weakened ecosystems. Additionally, the nation is vulnerable to floods, cyclones, and rising sea levels due to global climate change. Rising sea levels and cyclones threaten coastal communities’ livelihoods. These compromise access to safe water and agricultural productivity, affecting public health and food security [5].
As Bangladesh continues advancing its industrialization and economic development, it faces the potential of escalating environmental degradation, threatening the country’s Sustainable Development Goals (SDGs). Bangladesh is now concentrating on attaining the SDGs by 2030, specifically focusing on Climate Action and Life on Land, the 13th and 15th SDG [6]. Therefore, environmental deterioration has become a significant hurdle to sustainable development in Bangladesh [7]. The country faces the challenge of balancing economic growth with environmental sustainability while improving living standards, a complex issue that requires urgent attention from researchers, policymakers, and stakeholders [8].
As the economy grows, increased energy consumption drives development [9] but often leads to significant environmental degradation. Recently, the people of Bangladesh have increased their energy consumption in the form of fuel, gas, electricity, heavy pesticide use in the agriculture sector for hybrid production, rapid infrastructure development, etc., which in turn leads to adverse environmental impacts. This dynamic nature has garnered significant consideration from researchers yet remains insufficiently addressed in Bangladesh. By focusing on this nexus, our study offers valuable insights into the trade-offs between improving living standards and environmental sustainability. Identifying sustainable development strategies, this study helps to balance economic development with environmental conservation, ensuring a higher quality of life for all citizens in a rapidly changing world. Moreover, this research is unique in nature as it aims to fill the gap in knowledge in Bangladesh, which has not been previously studied.
2 Literature review
Environmental degradation threatens human health and well-being [10–12]. In their study, [10] highlighted the impact of environmental decline on human health, including increased vulnerability to infectious diseases, limited access to clean water and food, natural disasters, population relocation, and indirect effects like agricultural production and aquatic ecosystem deterioration. Thus, economic growth increases environmental degradation, which pollutes and harms people’s health. Similarly, modern ambient air pollution is a global health issue, according to [11]. [12] discussed how China’s air and water pollution, deforestation, and soil degradation affect public health and agriculture. The author stresses the link between environmental deterioration and natural dangers and calls for the Chinese government to act now to address the environmental issue. Some research studies the local effects of environmental degradation, notably indoor air pollution [13,14]. [13] found that indoor air pollution in Chittagong, Bangladesh, negatively affected the quality of life. [14] also explored how environmental decline affected Bangladeshi and Chinese quality of life. A portion of the scholarly investigation centers around the meticulous analysis of life expectancy inside the framework of environmental degradation [15,16]. The study revealed by [15] examines the effects of urban environmental deterioration on the overall welfare of Spanish miners, with a specific emphasis on mortality rates and variations in height. The analysis reveals elevated mortality rates in urban zones in Bilbao and Cartagena-La Union mining areas due to market deficiencies and unregulated urban development practices. [16] examined Bangladeshi life expectancy and environmental degradation. In Bangladesh, from 1974 to 2014, environmental deterioration negatively correlated with life expectancy, highlighting the necessity for environmental protection. [17] revealed that climate-related factors, such as rainfall, temperature, floods, and droughts, affect human development indicators in Sub-Saharan Africa. [18] studied the environmental impact of food waste on land resources and potential environmental benefits. They used the Food and Agriculture Organization’s methodology to evaluate food loss and waste, suggesting a sustainable approach.
Some studies analyzed environmental degradation’s causes and mitigation strategies, highlighting human activities, harmful gases, and the need for governmental intervention and stakeholder collaboration [19–22]. Energy use and production considerably increase greenhouse gas emissions, increasing climate change and global warming, emphasizing the need to understand the components involved [20]. Effective carbon dioxide mitigation policies require analyzing and anticipating these characteristics. A 1990–2019 study evaluates the causal association between emissions, economic progress, and energy use in India [21]. It reveals a unidirectional causality, providing new insights for policymakers to address India’s economic system and environmental challenges. [22] explains the association between life expectancy and environmental excellence, highlighting the positive correlation between longevity and environmental quality. The model also identifies multiple equilibria, including a trap with low life expectancy and environmental quality and its robustness in growth dynamics.
Some researchers analyzed the global perspectives on establishing standards to mitigate climate change’s adverse impacts [23–25]. The discussion revolves around the necessity of standards, potential challenges, and integrating climate change considerations into global frameworks. [23] argues that standards are necessary to identify and stabilize the phenomena of climate change, measure progress, and guide actions toward addressing the problem. However, the author also suggests that standards may lead us astray and that addressing climate change requires changing the political and epistemological climate. [24] suggest that the construction sector’s energy use, particularly in space heating, ventilation, and air conditioning, contributes to climate change. They propose integrating greenhouse gas reduction and climate change adaptation into building codes and standards. [25] explores the development of financial accounting standards for greenhouse gas emission allowances, focusing on the role of these standards in markets and their response to climate change. Green finance is a significant policy tool for combating climate change, but its direct impact on Carbon Dioxide (CO2) emissions has not been thoroughly studied.
Numerous studies have examined the impact of environmental degradation on peoples’ standard of living [10–18]. Nevertheless, the literature overlooks the vital role of living standard in deteriorating environment. While some studies have tried to explore the impact of living standard on environmental degradation, most of them focused on specific aspects of living standard [19–22]. An absence of comprehensive research examines the impact of multi-faceted influencing factors of living standard on environmental degradation. As far as we are aware, no prior research has comprehensively explored the impact of higher living standards on environmental degradation, thereby rendering this study a pioneering contribution to the academic discourse on environmental sustainability and living standards in Bangladesh. Additionally, this study fills the gap in the existing literature by introducing a unique methodological approach using an autoregressive distributed lag (ARDL) bounds test, error correction model (ECM), and Granger causality techniques simultaneously. Therefore, the conclusions of this study can provide practical implications for promoting living standard in developing countries like Bangladesh without harming environmental health.
3 Methodology
3.1 Variables
This study observes the complex connection between environmental degradation and living standards in Bangladesh using five variables: CO2 emissions per capita, GDP per capita, energy consumption (ENC), food production index (FPI), and life expectancy at birth (LEB). Here, CO2 emissions per capita is utilized as a proxy for environmental degradation, while GDP per capita, ENC, FPI, and LEB are used as a proxy for living standard. The annual time series data of all the variables except ENC spanning the years 1990 to 2021 are collected from the World Development Indicator (WDI) [26], and the information of ENC is gathered from Enerdata. The summary of the variables is presented in Table 1.
CO2 emission refers to releasing carbon dioxide into the atmosphere, primarily from burning fossil fuels, industrial processes, and deforestation. GDP per capita is calculated by dividing the GDP by the gross value added by all resident producers plus product taxes and minus subsidies by the midyear population. Energy consumption measures primary energy consumption before it is converted to other fuels, accounting for domestic production, imports, stock changes, exports, and fuel for international transport. The food production index tracks edible, nutrient-containing food crops, excluding non-nutritive items like coffee and tea. Life expectancy at birth estimates the number of years a newborn would live under current mortality rates throughout their life [26].
3.2 Econometric methodology
The study uses time series analytic methods such as Unit Root tests to check data stationarity, ARDL bound test to address cointegration, ECM to analyze how rapidly the system returns to its long-run equilibrium after a short-term shock, and Granger causality test to examine temporal relationships between variables. In addition, rigorous lag selection criteria have been employed to ascertain the most suitable lag structure, enhancing the model’s precision. The methodology also uses residual diagnostics such as Cumulative Sum (CUSUM) and Cumulative Sum Square (CUSUMSQ) tests to assess the model’s reliability and resolve issues.
ARDL model significantly mitigates the risk of erroneous regression when dealing with non-stationary series. According to [27], missing lags can be a preventive measure against erroneous regression caused by the absence of variables. The utilization of the ARDL model, as established by [28], is appropriate when the sample size is limited and the selected variables are joined in the same direction.
Given that the empirical model implicitly stated CO2 emission as a function of GDP, ENC, FPI, and LEB in Bangladesh, the following relationships will be investigated:
Where, CO2 stands for CO2 emissions per capita, GDP for GDP per person, ENC for energy consumption, FPI for food production index, and LEB for life expectancy at birth. It is possible to give an explicit form of Eq [1] as:
We convert all variables to natural logarithms to reduce multicollinearity and account for outliers. Here is the equation for the logarithm:
To assess whether the variables under study display cointegration in addition to long—and short-term dynamics, we use the ARDL bounds testing approach to Eq [4].
Where the optimum lag orders are x and y, Δ is the first difference operator, is the constant,
and
(i = 1–5) are the short- and long-run coefficients, and
is the autonomously and identically distributed disturbance term.
The bound test determines long-term variable associations. Here, the null hypothesis is stated as against the alternative hypothesis
. If the long-run relationship between the variables is recognized, the ECM estimates the short-run parameters. The ECM serves as a method for reconciling the short-term dynamics of an economic variable with its long-term dynamics. The study conducted by [29] demonstrates that the model under consideration encompasses insights into both short-run and long-run dynamics, focusing on the disequilibrium process to adjust toward the long-run equilibrium. So, the ECM analysis shows how rapidly the system returns to its long-term equilibrium after a short-term shock. The following equation serves as a depiction of the Error Correction (EC) formulation of the ARDL model:
Where ECTt−1 is the one-time lag in the EC term.
Based on the outcomes of Eq [5], it is expected that θ will be negative, falling somewhere between 0 and -1, since it signifies the extent to which equilibrium is restored. Akaike information criteria (AIC) determine the appropriate lag orders for each variable.
4 Results and discussions
4.1 Descriptive statistics and correlation analysis
Descriptive statistics give essential insight into the selected variables’ behavior and provide useful information about that behavior, as shown in Table 2.
The mean CO2 emissions per capita value is -1.404 metric tons, signifying the low CO2 emission level. The logarithmic transformation compresses the scale of data, so a negative mean in this context indicates that, on average, per capita CO2 emissions in Bangladesh are relatively low when transformed back into the original scale. Likewise, for GDP per capita, we find a mean of US$ 6.467; for energy consumption (ENC), it is 10.098 kilotons of oil equivalent; for the food production index (FPI), it is 4.276; and for life expectancy at birth (LEB), it is 4.187 years. In our dataset, the kurtosis values for CO2 emission per capita, GDP per capita, ENC, FPI, and LEB exhibit platykurtic distributions, indicating flattened curves with values lower than the sample mean. From the Jarque-Bera probability value in Table 2, we accept the null hypothesis that all variables are normally distributed since their p-values are larger than the 5% significance level.
Table 3 shows the correlation between the variables, revealing that the pairwise variables exhibit strong positive correlations.
As there are strong positive relationships between each pair of the variables, further analysis is needed to determine the long-run characteristics of these relationships.
4.2 Unit root tests
The estimation process for the ARDL technique can commence after ensuring that all data series have been transformed into stationary form. The Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests are commonly employed to assess unit root issues in the chosen data series (Table 4).
In Table 4 both ADF and PP tests exhibit that LNGDP and LNFPI are non-stationary, while LNENC is stationary at level I(0). Although LNCO2 is found to be non-stationary in the ADF test, it appears stationary in the PP test. Conversely, LNLEB is stationary according to the ADF test but non-stationary according to the PP test. However, all variables are stationary according to ADF and PP tests after applying a first-order differencing transformation to the LNCO2, LNGDP, LNENC, LNFPI, and LNLEB series. We use this treatment to find that time series data are steady at the 5% significance level, indicating no unit root issues at first difference.
4.3 Lag length criteria
Before applying the ARDL model, it is crucial to determine the optimal lag length for the study variables. Selecting the appropriate lags is vital, as incorrect choices can result in inconsistent outcomes unsuitable for policy analysis. The Akaike Information Criterion (AIC) and Schwarz Criterion (SC) are two well-known methods used to identify the most appropriate lags (Table 5).
The optimal lag for this analysis is determined using the AIC criterion. According to Table 5, lag 1 is best suited to our sample size and has been selected as the optimal lag.
4.4 Result of the ARDL model
This study examines the impact of Bangladesh’s GDP per capita, ENC, FPI, and LEB on CO2 emissions per capita using the ARDL model. This model estimates the variables’ short-term dynamics and long-term coefficients. Table 6 displays the results of the estimated ARDL model.
Following previous studies [30,31], in our analysis with EViews, the software automatically determines the best-fitting model by default to minimize overfitting issues. Thus, the (1, 0, 0, 0, 1) specification aligns with AIC’s best fit for individual variables after considering the optimal lag 1.
The lagged value of CO2 emissions is denoted by LNCO2(-1). A positive coefficient of 0.327 suggests that a 1% increase in CO2 emissions from the previous period results in a 0.327% increase in current CO2 emissions, holding all other variables constant. This implies a momentum effect in CO2 emissions. GDP has a negative coefficient (-0.150), which means that a 1% increase in GDP results in a 0.150% decrease in CO2 emissions. This results from the transition to cleaner technologies and energy efficiency enhancement as economies develop. The positive coefficient (1.175) for energy consumption indicates that a 1% increase in energy consumption results in a 1.175% increase in CO2 emissions. This happens as increased energy consumption frequently leads to increased emissions. GDP and ENC are both significant variables, and previous studies support our findings [32,33].
A 1% increase in the food production index (FPI) results in a 0.019% decrease in CO2 emissions, as indicated by the negative coefficient (-0.019) for the FPI. The positive coefficient (0.464) for life expectancy at birth (LEB) suggests that a 1% increase in LEB results in a 0.464% increase in CO2 emissions. The negative lagged coefficient (-0.570) for LEB indicates that a 1% increase in the LEB of the previous period results in a 0.570% decrease in present CO2 emissions, provided that other factors remain constant.
The high R-squared value of 0.99 suggests that GDP, ENC, FPI, and LEB explain 99% of CO2 emissions’ changes, leaving 1% to components not included in the model. The p-value of the F-statistic (1720.136) is below 5%, making the overall regression statistically significant.
4.5 ARDL bound test
The ARDL bound test can establish a long-term association between CO2 emission and the explanatory variables. The results of the ARDL bound test are summarized in Table 7.
Based on Table 7, the F-statistic stands at 18.26, surpassing critical values across all significance levels, thus rejecting the null hypothesis. This suggests that at the 5% significance threshold, a long-run connection exists between the CO2 emissions (LNCO2) and its regressors.
In the long-run model, estimated coefficients of LNGDP, LNENC, LNFPI, and LNLEB are found as -0.22, 1.75, -0.03, and -0.16, respectively. Besides, LNGDP and LNENC have statistically significant effects on CO2 emissions (Table 8).
GDP per capita and LEB are negatively correlated with CO2 emissions per capita in Bangladesh. A one percent increase in GDP per capita and LEB decreases CO2 emissions per capita by 0.22 percent and 0.16 percent, respectively, on average, with the remaining variables constant. These negative relationships of GDP and life expectancy at birth with CO2 emissions suggest that as the country becomes wealthier and healthier, people become more aware and adopt cleaner technologies, reducing CO2 emissions. Although previous studies support this finding, some studies have found positive impacts of GDP and LEB on CO2 emissions [32,34,35]. According to [32], higher GDP enables investment in renewable energy and better healthcare, improving life expectancy while decreasing reliance on fossil fuel, thus reducing CO2 emissions. Also, Bangladesh’s FPI is negatively correlated with CO2 emissions per capita. An increase of 1% in FPI decreases per capita carbon dioxide emissions on an average by 0.03 percent, while other variables are unchanged. In Bangladesh, a negative relationship between the FPI and CO2 emissions suggests that as food production becomes more efficient or sustainable, CO2 emissions decrease, due to the adoption of eco-friendly agricultural practices. The previous study of [36] also claims that reduced agricultural productivity significantly increases CO2 emissions, contributing to environmental degradation, supporting our insight. On the other hand, Bangladesh’s ENC is positively correlated with CO2 emissions per capita. An increase of 1% in ENC raises per capita carbon dioxide emissions on an average by 1.75 percent, when all other variables remain constant. ENC is positively related to CO2 emissions because its activities often rely heavily on fossil fuels. Our findings align with a similar study [37], stating that increased energy consumption typically means more burning of coal, oil, and gas, which releases CO2. These outcomes provide strong evidence of the impact of living standards on the environment.
4.6 Result of ECM
According to the ECM result, long-term equilibrium will be restored if there are temporary discrepancies in CO2 emissions relative to GDP, ENC, FPI, or LEB. Table 9 shows the results of the ARDL model implemented in ECM.
The EC term in this approximation is written as CointEq(-1)*. At the 5% level, the EC term is significant and has a negative sign. It suggests a reversion to long-run equilibrium. It also implies that a deviation from the equilibrium level of CO2 emission per capita during the current period will be corrected by 67.30 percent in the next period, demonstrating a rapid reversion to stability after any short-term shocks. This high adjustment speed supports a resilient equilibrium relationship between the variables over time.
The short-run results (ECM) suggest that LEB is positively but insignificantly correlated with CO2 emissions per capita in Bangladesh. A one percent increase in LEB also increases CO2 emissions per capita by 0.46 percent, on average, ceteris paribus. This happens due to improvements in healthcare and living standards, which lead to more economic activities, which increase emissions. Which is similar to the previous result [38], and also the previous study claims that in short-run, there is a negative relationship between CO2 and LEB [33].
4.7 Granger causality test
The analysis in this study employs the Granger causality test to investigate the temporal dynamics among the variables under consideration. Granger causality is a statistical concept used to determine whether one time series can predict or “cause” another time series in a specific sense. It does not imply a direct causal relationship in the scientific sense (i.e., one variable causing changes in another). Instead, it suggests that knowing past values of one variable helps improve the prediction of another variable. The results of the Granger causality test are shown in Table 10.
From Table 10, at the 5% significance level, causal analysis rejects the null hypothesis that LNCO2 does not function as a Granger cause for LNGDP, demonstrating a one-way causation from LNCO2 to LNGDP. However, between LNENC and LNCO2, there found no causal relationship at 5%. A two-way causation exists between LNCO2 and LNFPI. The two-way causation between LNCO2 and LNFPI is also found in the previous study [39]. On the other hand, no causal relationship is found between LNCO2 and LNLEB.
4.8 Diagnostics and stability test results
Residual diagnostics check if a model displays serial correlation, heteroscedasticity, abnormal distribution, etc., which can cause faulty conclusions when analyzing data. The results of diagnostics and stability tests are exhibited in Table 11.
From Table 11 the Lagrange Multiplier (LM) test shows that the model is robust and has no autocorrelation. The Breusch-Pagan-Godfrey (BPG) test measures heteroscedasticity. It shows homoscedastic residuals with a p-value of 0.37. Also, the p-value of Jarque-Bera test shows that the model exhibits normal distribution.
We use CUSUMSQ to test the consistency of the parameters. The CUSUMSQ test detects modest regression coefficients with significant changes and shows parameter stability.
In the graph (See Fig 1), the straight lines represent the crucial boundaries at the 5% significance level. The CUSUMSQ line stays inside the 5% significant bounds, showing that the coefficients stayed stable during the sample period.
5 Conclusion and policy recommendations
From the Sustainable Development Goals 13 and 15, climate change is a crucial issue for Bangladesh. The study maintains a two-step approach, presenting descriptive statistics and applying econometric models to analyze the relationship between environmental degradation and living standard in Bangladesh. The study uses CO2 emissions per capita as a proxy for environmental deterioration and GDP per capita, energy consumption, food production index, and life expectancy at birth for living standards. The study shows a significant positive long-term effect of energy consumption on CO2 emissions per capita in Bangladesh, while GDP confirms a negative impact. However, the LEB and the FPI do not significantly impact CO2 emissions over time. The ECM confirms a long-run relationship, resolving deviations from equilibrium by 67.30 percent within a year. Moreover, the Granger causal analysis reveals a bidirectional relationship between CO2 emissions per capita and food production index, however, no causal association was found from GDP per capita, energy consumption, and life expectancy at birth to CO2 emissions per capita at 5% significance level.
For policy recommendations based on findings the study suggests that the government should invest in renewable energy sources like solar and wind to reduce CO2 emissions and promote energy-efficient technologies to lower overall consumption. The country can give more emphasize on increasing GDP. Higher GDP enables investment in renewable energy, and cleaner technologies. Moreover, economic growth strategies should include green investments, incentives for sustainable industrial practices, which can lower emissions as the economy grows. Implementation of carbon pricing can incentivize sustainable practices. In addition, healthcare and environmental quality should be improved to raise life expectancy, as healthier populations often exhibit lower environmental footprints. In the agricultural sector, enhancing forest conservation and supporting eco-friendly agricultural practices can balance GDP growth with environmental protection. These measures can help Bangladesh achieve a balance between improved living standards and reduced environmental degradation.
In this study, CO2 emission is used as a proxy variable of environmental degradation, while GDP, ENC, LEB, and FPI are used as a proxy of living standards. This may not fully capture the dynamic complexity between environmental degradation and living standard. Future research could use better methods or include more variables for a deeper analysis. Further study could also address the effects of renewable energy on the environment in Bangladesh.
Acknowledgments
We are grateful to our esteemed departmental mentors for their advice and insightful feedback during this research project.
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