This study examines the impact of energy consumption, urbanization, and globalization on environmental degradation proxied by carbon emissions (CO2) in the South Asian Association for Regional Cooperation (SAARC) countries, namely Sri Lanka, Pakistan, Maldives, Nepal, Bhutan, Bangladesh, and India using data over the period 1990-2018. The cross-sectional autoregressive distributed lag (CS-ARDL), pooled mean group (PMG), and Dumitrescu and Hurlin (D-H) Granger causality techniques are employed for the empirical analysis. First and second-generation panel unit root tests are used to determine the stationary level of all data series which reveals mixed order of integration. The empirical findings show that urbanization, gross domestic product (GDP) per capita income, energy consumption, industrial growth, globalization, and financial development cause CO2 emissions, while the other variables, namely arable land and innovation, put negative effects on CO2 emissions. Moreover, the D-H heterogeneous test results exhibit that bi-directional relationship exists between CO2 and arable land, urbanization, industrial growth, and financial development, while a unidirectional causality exists between CO2 emissions and GDP per head income. These findings suggest that planned urbanization, investment in renewable energy sources, and effective strategies regarding the economic and financial integration with the global economies are required for a clean and green environment.
This paper assesses the Environmental Kuznets curve based on quantile behavior of the relationship between economic growth, forest area, agriculture production, renewable energy, and environmental degradation. The current literature generally used a single indicator to address environmental issues; however single indicator neither measures overall environmental conditions nor does specify that the environment issue is generally diminishing. Our study is the first one that used ecological footprint (EF) as an indicator to test environmental Kuznets curve (EKC) hypothesis for Pakistan by employing recent approach of quantile autoregressive distributed lag (QARDL) initiated by Cho et al. (J Econ 188(1):281-300, 2015). The result of this study validates the EKC hypothesis for Pakistan and shows quantile-dependent relationship, and in that case, using the conventional methods may somewhat lead to biased results. Moreover, the rejection of the null hypothesis of parameter constancy is also confirmed by Wald test. In the long run, the findings of renewable energy consumption and forest area show significant negative effects on ecological footprints, which indicates that by increasing renewable energy usage and forest area, ecological footprints can be minimized. Interestingly, the short-term effects of agricultural production findings on EF show statistically negative results. This illustrates that EF can also be reduced in the agriculture sector by adopting environment-friendly technologies. In order to create efficient policies for environment deterioration, the empirical findings of the current analysis can be used as a guideline for policy implications.
The aim of this study was to optimize the antioxidant activity of Piper nigrum L. essential oil extracted using the supercritical carbon dioxide (SC-CO₂) technique. Response surface methodology was applied using a three-factor central composite design to evaluate the effects of three independent extraction variables: pressure of 15-30 MPa, temperature of 40-50 °C and dynamic extraction time of 40-80 min. The DPPH radical scavenging method was used to evaluate the antioxidant activity of the extracts. The results showed that the best antioxidant activity was achieved at 30 MPa, 40 °C and 40 min. The extracts were analyzed by GC-FID and GC-MS. The main components extracted using SC-CO₂ extraction in optimum conditions were β-caryophyllene (25.38 ± 0.62%), limonene (15.64 ± 0.15%), sabinene (13.63 ± 0.21%), 3-carene (9.34 ± 0.04%), β-pinene (7.27 ± 0.05%), and α-pinene (4.25 ± 0.06%). The essential oil obtained through this technique was compared with the essential oil obtained using hydro-distillation. For the essential oil obtained by hydro-distillation, the most abundant compounds were β-caryophyllene (18.64 ± 0.84%), limonene (14.95 ± 0.13%), sabinene (13.19 ± 0.17%), 3-carene (8.56 ± 0.11%), β-pinene (9.71 ± 0.12%), and α-pinene (7.96 ± 0.14%). Radical scavenging activity of the extracts obtained by SC-CO₂ and hydro-distillation showed an EC₅₀ of 103.28 and 316.27 µg mL(-1) respectively.
The well-established emissions-growth debate relies on the symmetric nexus between CO2 emissions and economic growth, thereby ignoring a fundamental component of macro economy in the form of asymmetric relation. This paper considers how CO2 emissions respond asymmetrically to changes in economic growth. While utilizing both linear and nonlinear time series approaches for an environmentally exposed country, Pakistan over the period 1971-2018, we find convincing evidence that CO2 emissions rise more rapidly during negative shocks to economic growth than increase during economic expansions. Thus, contrary to what has previously been reported, the effect is strong as holds both at short run and long run. This is partly due to the increase in informal sector as GDP declines. Our estimated results show that accounting for the shadow economy results a higher magnitude of CO2 emissions due to decrease in economic growth, thus question the traditional symmetric decoupling of economic growth and CO2 emissions. The estimated results are robust to alternative estimators such as fully modified least squares (FMOLS) and dynamic OLS (DOLS). Thus, the findings of this study call for a re-thinking on climate policy design that rarely pays attention to the aforementioned outcomes due to fall in economic growth.
It is well documented that carbon emissions can be reduced by replacing conventional energy resources with renewable energy resources; thereby, the role of green technology is essential as it protect natural environment. Given that, the United Nations' agenda of "green is clean" may be achievable by adoption of green technologies. The objective of the study is to examine the link between information and communication technology (ICT), economic growth, energy consumption, and carbon dioxide (CO2) emissions in the context of South Korean economy, by using a novel Morlet wavelet approach. The study applies continuous wavelet power spectrum, the wavelet coherency, and the partial and the multiple wavelet coherency to the year during 1973-2016. The outcomes reveal that the connections among the stated variables progress over frequency and time domain. From the frequency domain point of view, the current study discovers noteworthy wavelet coherence and robust lead and lag linkages. From the time-domain sight, the results display robust but not consistent associations among the considered variables. From an economic point sight, the wavelet method displays that ICT helps to reduce environmental degradation in a medium and long run in the South Korean economy. This emphasizes the significance of having organized strategies by the policymakers to cope up with 2 to 3 years of the occurrence of the huge environmental degradation in South Korea.
The current study examines sustainable electricity consumption for economic growth in a small open and tourist economy. The energy-tourism nexus is evaluated for the relationship between sustainable electricity consumption and the international tourist arrival for the South African economy. The present study leverages on annual frequency data for South Africa from 1995 to 2019 for empirical analysis using the ARDL technique. Accordingly, empirical findings indicate a significant direct connection between the sustainable electricity consumption and the international tourism arrival; the study affirms that tourism-induced energy hypothesis is valid in South Africa. However, from a policy standpoint, alternative energy efficiency mechanisms such as renewable energy systems and emancipation of current energy management capabilities are recommended in South Africa. This is necessary for sustainable eco-friendly tourism that engenders clean energy consumption for the study area. More insights into policy caveats are presented in the concluding section.
The main objective of this paper is to estimate the interfuel substitution elasticities between hydropower and the fossil fuels of coal and natural gas used in the generation of electricity for Malaysia. Due to the violation of the assumption behind the ordinary least squares (OLS) method on account of the correlated error terms in the system of equations, the econometrics techniques of seemingly unrelated regression (SUR) was adopted to obtain the parameter estimates using dataset that covers the period 1988 to 2016. The main finding is that there exists substantial substitution possibility between hydropower and fossil fuels in the generation of electricity for Malaysia. CO2 emissions mitigation scenarios were also conducted to explore the possible effects of substituting fossil fuels for hydropower to generate electricity. The results show that switching from high carbon-emitting fuels to renewable energy such as hydropower will substantially reduce CO2 emission and assist the country towards achieving the carbon emissions reduction targets. Policy recommendations are offered in the body of the manuscript.
To boost the stability of economic and financial aspects along with the apprehensions for sustainability, it is important to promote the development of clean energy stocks around the globe. In the current research, the researchers have examined the impact of oil prices, coal prices, natural gas prices, and gold prices on clean energy stock using the autoregressive distribution lag (ARDL) approach from the year 2011 to the year 2020. The result of daily data analysis specifies that in the long as well as in the short run, gold prices, oil prices, and coal prices have a positive and significant effect on clean energy stock. On the other side, natural gas prices in the long as well as in the short run have a negative and significant effect on clean energy stock. So, the empirical analysis of our study is of interest to investors at an institutional level who aim at detecting the risk associated with the clean energy market through proper financial modeling. Besides, this study opens up a new domain to sustain financial as well as economic prospects by protecting the environment through clean energy stock as the investment in clean energy stocks results in producing a substantial effect on the economy and the environment as well.
This study examines the impact of government spending, income, and tourism consumption on CO2 emissions in the 50 US states through a novel theoretical model derived from the Armey Curve model and the Environmental Kuznets Curve hypothesis. The findings of this research are essential for policymakers to develop effective strategies for mitigating environmental pollution. Utilizing panel cointegration analysis, the study provides valuable insights into whether continued increases in government spending contribute to higher pollution levels. By identifying the threshold point of spending as a percentage of GDP, policymakers can make informed decisions to avoid the trade-off between increased spending and environmental degradation. For instance, the analysis reveals that Hawaii's tipping point is 16.40%. The empirical results underscore the importance of adopting sustainable policies that foster economic growth while minimizing environmental harm. These findings will aid policymakers in formulating targeted and efficient approaches to tackle climate change and promote long-term environmental sustainability in the United States. Moreover, the impact of tourism development on CO2 emissions varies across states, with some US states experiencing a decrease while others see an increase.
Information on temporal and spatial variations in soil greenhouse gas (GHG) fluxes from tropical peat forests is essential to predict the influence of climate change and estimate the effects of land use on global warming and the carbon (C) cycle. To obtain such basic information, soil carbon dioxide (CO2) and methane (CH4) fluxes, together with soil physicochemical properties and environmental variables, were measured at three major forest types in the Maludam National Park, Sarawak, Malaysia, for eight years, and their relationships were analyzed. Annual soil CO2 fluxes ranged from 860 to 1450 g C m⁻2 yr⁻1 without overall significant differences between the three forest sites, while soil CH4 fluxes, 1.2-10.8 g C m⁻2 yr⁻1, differed. Differences in GHG fluxes between dry and rainy seasons were not necessarily significant, corresponding to the extent of seasonal variation in groundwater level (GWL). The lack of significant differences in soil CO2 fluxes between the three sites could be attributed to set-off between the negative and positive effects of the decomposability of soil organic matter as estimated by pyrophosphate solubility index (PSI) and GWL. The impact of El-Niño on annual CO2 flux also varied between the sites. The variation in soil CH4 fluxes from the three sites was enhanced by variations in temperature, GWL, PSI, and soil iron (Fe) content. A positive correlation was observed between the annual CH4 flux and GWL at only one site, and the influence of soil properties was more pronounced at the site with the lowest GWL and the highest PSI. Variation in annual CH4 fluxes was controlled more strongly by temperature where GWL was the highest and GWL and plant growth fluctuations were the least. Inter-annual variations in soil CO2 and CH4 fluxes confirmed the importance of long-term monitoring of these at multiple sites supporting different forest types.
The disastrous consequences of climate change for human life and environmental sustainability have drawn worldwide attention. Increased global warming is attributed to anthropogenic greenhouse gas (GHG) emissions, biodiversity loss, and deforestation due to industrial output and huge consumption of fossil fuels. Financial inclusion can be acted as an adaptation or a mitigation measure for environmental degradation. This study analyzed the impact of financial inclusion on environmental degradation in OIC countries for the period 2004-2018. A novel approach, "Dynamic Common Correlated Effects (DCCE)" is used to tackle the problem of heterogeneity and cross-sectional dependence (CSD). Various GHG emissions along with deforestation and ecological footprint are used as indicators of environmental degradation. Long-run estimation confirms that financial inclusion is positively and significantly linked with CO2 emission, CH4 emission, and deforestation while negatively correlated with ecological footprint and N2O emission in overall and higher-income OIC economies. An inverted U-shaped environmental Kuznets curve (EKC) is validated when ecological footprint, CO2, and CH4 are used in all panels of OIC countries. An inverted U-shaped EKC is also observed for deforestation in lower-income and overall OIC countries. In the case of N2O emission, however, a U-shaped EKC appears in lower-income and overall OIC countries. It is suggested that the governments of OIC countries should continue to have easy access to financial services and maintain sustainable use of forests and biocapacity management to address environmental challenges.
Environmental degradation is significantly studied both in the past and the current literature; however, steps towards reducing the environmental pollution in carbon emission and haze pollution like PM2.5 are not under rational attention. This study tries to cover this gap while considering the carbon emission and PM2.5 through observing the role of renewable energy, non-renewable energy, environmental taxes, and ecological innovation for the top Asian economies from 1990 to 2017. For analysis purposes, this research considers cross-sectional dependence analysis, unit root test with and without structural break (Pesaran, 2007), slope heterogeneity analysis, Westerlund and Edgerton (2008) panel cointegration analysis, Banerjee and Carrion-i-Silvestre (2017) cointegration analysis, long-short run CS-ARDL results, as well as AMG and CCEMG for robustness check. The empirical evidence in both the short- and long-run has confirmed the negative and significant effect of renewable energy sources, ecological innovation, and environmental taxes on carbon emissions and PM2.5. Whereas, non-renewable energy sources are causing environmental degradation in the targeted economies. Finally, various policy implications related to carbon emission and haze pollution like PM2.5 are also provided to control their harmful effect on the natural environment.
Global warming is attracting attention from policy makers due to its impacts such as floods, extreme weather, increases in temperature by 0.7°C, heat waves, storms, etc. These disasters result in loss of human life and billions of dollars in property. Global warming is believed to be caused by the emissions of greenhouse gases due to human activities including the emissions of carbon dioxide (CO2) from petroleum consumption. Limitations of the previous methods of predicting CO2 emissions and lack of work on the prediction of the Organization of the Petroleum Exporting Countries (OPEC) CO2 emissions from petroleum consumption have motivated this research.
Conversion of tropical peat swamp forest to drainage-based agriculture alters greenhouse gas (GHG) production, but the magnitude of these changes remains highly uncertain. Current emissions factors for oil palm grown on drained peat do not account for temporal variation over the plantation cycle and only consider CO2 emissions. Here, we present direct measurements of GHGs emitted during the conversion from peat swamp forest to oil palm plantation, accounting for CH4 and N2O as well as CO2. Our results demonstrate that emissions factors for converted peat swamp forest is in the range 70-117 t CO2 eq ha-1 yr-1 (95% confidence interval, CI), with CO2 and N2O responsible for ca. 60 and ca. 40% of this value, respectively. These GHG emissions suggest that conversion of Southeast Asian peat swamp forest is contributing between 16.6 and 27.9% (95% CI) of combined total national GHG emissions from Malaysia and Indonesia or 0.44 and 0.74% (95% CI) of annual global emissions.
Tropical peatlands are globally important ecosystems with high C storage and are endangered by anthropogenic disturbances. Microbes in peatlands play an important role in sustaining the functions of peatlands as a C sink, yet their characteristics in these habitats are poorly understood. This research aimed to elucidate the responses of these complex ecosystems to disturbance by exploring greenhouse gas (GHG) emissions, nutrient contents, soil microbial communities and the functional interactions between these components in a primary and secondary peat swamp forest in Peninsular Malaysia. GHG measurements using closed chambers, and peat sampling were carried out in both wet and dry seasons. Microbial community phenotypes and nutrient content were determined using phospholipid fatty acid (PLFA) and inductively-coupled plasma mass spectrometry (ICP-MS) analyses respectively. CO2 emissions in the secondary peat swamp forest were > 50% higher than in the primary forest. CH4 emission rates were ca. 2 mg m-2 h-1 in the primary forest but the secondary forest was a CH4 sink, showing no seasonal variations in GHG emissions. Almost all the nutrient concentrations were significantly lower in the secondary forest, postulated to be due to nutrient leaching via drainage and higher rates of decomposition. Cu and Mo concentrations were negatively correlated with CO2 and CH4 emissions respectively. Microbial community structure was overwhelmingly dominated by bacteria in both forest types, however it was highly sensitive to land-use change and season. Gram-positive and Gram-negative relative abundance were positively correlated with CO2 and CH4 emissions respectively. Drainage related disturbances increased CO2 emissions, by reducing the nutrient content including some with known antimicrobial properties (Cu & Na) and by favouring Gram-positive bacteria over Gram-negative bacteria. These results suggest that the biogeochemistry of secondary peat swamp forest is fundamentally different from that of primary peat swamp forest, and these differences have significant functional impacts on their respective environments.
Environmental sustainability is a key target to achieve sustainable development goals (SDGs). However, achieving these targets needs tools to pave the way for achieving SDGs and COP28 targets. Therefore, the primary objective of the present study is to examine the significance of clean energy, research and development spending, technological innovation, income, and human capital in achieving environmental sustainability in the USA from 1990 to 2022. The study employed time series econometric methods to estimate the empirical results. The study confirmed the long-run cointegrating relationship among CO2 emissions, human capital, income, R&D, technological innovation, and clean energy. The results are statistically significant in the short run except for R&D expenditures. In the long run, the study found that income and human capital contribute to further aggravating the environment via increasing CO2 emissions. However, R&D expenditures, technological innovation, and clean energy help to promote environmental sustainability by limiting carbon emissions. The study recommends investment in technological innovation, clean energy, and increasing R&D expenditures to achieve environmental sustainability in the USA.
The purpose of this study was to determine the indoor air quality (IAQ) status of an automotive assembly plant in Rawang, Selangor, Malaysia using selected IAQ parameters, such as carbon dioxide (CO2), carbon monoxide (CO), temperature, relative humidity (RH) and respirable particulate matter (PM10). A cross-sectional study was conducted in the paint shop and body shop sections of the plant in March 2005. The Q-TRAK Plus IAQ Monitor was used to record the patterns of CO, CO2, RH and temperature; whilst PM10 was measured using DUSTTRAK Aerosol Monitor over an 8-hour time weight average (8-TWA). It was found that the average temperatures, RH and PM10 in the paint shop section and body shop sections exceeded the Department of Safety and Health (DOSH) standards. The average concentrations of RH and CO were slightly higher in the body shop section than in the paint shop section, while the average concentrations of temperature and CO2 were slightly higher in the paint shop section than in the body shop section. There was no difference in the average concentrations of PM10 between the two sections.
This paper investigates the non-linear impacts of the agricultural, industrial, financial, and service sectors on environmental pollution in Malaysia during the 1980-2018 period. It employs the extended STIRPAT model and two indicators of environmental pollution (carbon dioxide emissions and ecological footprints). It uses the autoregressive distributed lag (ARDL) technique to estimate the parameters. Evidence from the study indicate that the agricultural, industrial, and service sectors have inverted U-shaped non-linear impacts on carbon dioxide emissions and ecological footprints, while the financial sector has a U-shaped non-linear relationship with carbon dioxide emissions and ecological footprint. These empirical outcomes are robust to diagnostic tests, structural breaks, and alternative estimation technique and proxies. The economic implication of this paper is that, at the early stage of sectoral growth, the pollution intensity of sectoral output increases, but after a certain turning point, a further increase in sectoral output will reduce environmental pollution. Precisely, environmental pollution will reduce if the agricultural, industrial, and service sectors exceed threshold levels of 11%, 44%, and 49% of GDP, respectively, while environmental pollution will be aggravated if financial sector exceeds a threshold level of 94%. Therefore, efforts to mitigate environmental pollution in Malaysia should integrate sectoral growth to attain sustainable development.
In light of a slow buildup in CO2 emissions since the recovery, this paper revisits the relationship between CO2 emissions and the US economy using a nonlinear autoregressive distributed lag model, in which the determinants are identified through an expanded real business cycle model. We find convincing evidence that CO2 emissions decline more rapidly during recessions than increase during expansions over the long run. Of all determinants considered, long-run asymmetry is fostered once vehicle miles traveled is controlled. This calls for a greater attention to public transportation development and vehicle miles traveled tax for slowing down stock buildup of CO2 emissions during good times.