An innovative approach was developed by incorporating high-pressure CO2 into the separate hydrolysis-fermentation of aspen leftover branches, aiming to enhance the bioethanol production efficiency. The high-pressure CO2 significantly increased the 72-h enzymatic hydrolysis yield of converting aspen into glucose from 53.8% to 82.9%. The hydrolysis process was performed with low enzyme loading (10 FPU g-1 glucan) with the aim of reducing the cost of fuel bioethanol production. The ethanol yield from fermentation of the hydrolyzed glucose using yeast (Saccharomyces cerevisiae) was 8.7 g L-1, showing increment of 10% compared with the glucose control. Techno-economic analysis indicated that the energy consumption of fuel bioethanol production from aspen branch chips was reduced by 35% and the production cost was cut 44% to 0.615 USD L-1, when 68 atm CO2 was introduced into the process. These results furtherly emphasized the low carbon footprint of this sustainable energy production approach.
As the world's second largest palm oil producer and exporter, Malaysia could capitalize on its oil palm biomass waste for power generation. The emission factors from this renewable energy source are far lower than that of fossil fuels. This study applies an integrated carbon accounting and mitigation (INCAM) model to calculate the amount of CO2 emissions from two biomass thermal power plants. The CO2 emissions released from biomass plants utilizing empty fruit bunch (EFB) and palm oil mill effluent (POME), as alternative fuels for powering steam and gas turbines, were determined using the INCAM model. Each section emitting CO2 in the power plant, known as the carbon accounting center (CAC), was measured for its carbon profile (CP) and carbon index (CI). The carbon performance indicator (CPI) included electricity, fuel and water consumption, solid waste and waste-water generation. The carbon emission index (CEI) and carbon emission profile (CEP), based on the total monthly carbon production, were determined across the CPI. Various innovative strategies resulted in a 20%-90% reduction of CO2 emissions. The implementation of reduction strategies significantly reduced the CO2 emission levels. Based on the model, utilization of EFB and POME in the facilities could significantly reduce the CO2 emissions and increase the potential for waste to energy initiatives.
Most researchers focused on developing highly selective membranes for CO2/CH4 separation, but their developed membranes often suffered from low permeance. In this present work, we aimed to develop an ultrahigh permeance membrane using a simple coating technique to overcome the trade-off between membrane permeance and selectivity. A commercial silicone membrane with superior permeance but low CO2/CH4 selectivity (in the range of 2-3) was selected as the host for surface modification. Our results revealed that out of the three silane agents tested, only tetraethyl orthosilicate (TEOS) improved the control membrane's permeance and selectivity. This can be due to its short structural chain and better compatibility with the silicone substrate. Further investigation revealed that higher CO2 permeance and selectivity could be attained by coating the membrane with two layers of TEOS. The surface integrity of the TEOS-coated membrane was further improved when an additional polyether block amide (Pebax) layer was established atop the TEOS layer. This additional layer sealed the pin holes of the TEOS layer and enhanced the resultant membrane's performance, achieving CO2/CH4 selectivity of ~19 at CO2 permeance of ~2.3 × 105 barrer. This performance placed our developed membrane to surpass the 2008 Robeson Upper Boundary.
Climate change caused by different anthropogenic activities is a subject of attention globally. There is a concern on how to maintain a clean environment and at the same time achieve optimal use of land. To this end, this study examines the causal effects of land use including agricultural, forestry, and other land categories on greenhouse gas (GHG) emissions. The data for China is collected over the period 1990 to 2012 for the empirical examination. By employing vector error correction model (VECM), it is found that there is significant long-run causality among variables. However, in the short run expectedly, only land under agriculture has strong causality with the GHG emissions. The results in case of variance decomposition analysis highlight that land under agriculture and other use significantly causes the GHG emissions in the long run. Further, impulse responses of variables are also measured with the Cholesky one standard deviation. The results are robust and support the argument that different land uses cause GHG emissions in China. The study provides insights for policy makers to improve the activities occurring on agricultural and other land uses. Assessment of overall potential, including bio energy, needs to include analysis of trade-offs and feedbacks with land-use competition. Many positive linkages with sustainable development and with adaptation exist but are case and site specific as they depend on scale, scope, and pace of implementation.
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.
Being closely correlated with income and economic growth, trade openness impacts the environmental quality through different means. The study analyzes the robustness of the environmental Kuznets curve (EKC) hypothesis in OIC countries by examining the extent to which trade openness influence environmental quality through different environmental indicators for the period 1991 to 2018. A new methodology dynamic common correlated effects (DCCE) is applied to resolve the issue of cross-sectional dependence (CSD). We have used greenhouse gas (GHG) emissions, carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) along with ecological footprint as indicators of environmental quality. Results of DCCE estimation identify a negative association of trade openness with CO2, N2O, and CH4, while the positive relationship with the ecological footprint in overall OIC countries and higher income OIC countries. On the other hand, trade openness has a positive association with all environmental indicators in lower income OIC countries. Our findings confirm that inverted-U-shaped EKC exists in all groups of OIC countries when CO2, CH4, and ecological footprint are used as environmental indicators. However, a U-shaped EKC exists in overall OIC countries and lower income OIC countries when N2O is used. Eventually, it is recommended that if OIC countries continue trade openness policies and energy sector reforms and maintain sustainable use of biocapacity; then, they will be able to combat environmental issues with the increase in income.
This work is scrutinizing the development of metallized biochar as a low-cost bio-sorbent for low temperature CO2 capture with high adsorption capacity. Accordingly, single-step pyrolysis process was carried out in order to synthesize biochar from rambutan peel (RP) at different temperatures. The biochar product was then subjected to wet impregnation with several magnesium salts including magnesium nitrate, magnesium sulphate, magnesium chloride and magnesium acetate which then subsequently heat-treated with N2. The impregnation of magnesium into the biochar structure improved the CO2 capture performance in the sequence of magnesium nitrate > magnesium sulphate > magnesium chloride > magnesium acetate. There is an enhancement in CO2 adsorption capacity of metallized biochar (76.80 mg g-1) compare with pristine biochar (68.74 mg g-1). It can be justified by the synergetic influences of physicochemical characteristics. Gas selectivity study verified the high affinity of biochar for CO2 capture compared with other gases such as air, methane, and nitrogen. This investigation also revealed a stable performance of the metallized biochar in 25 cycles of CO2 adsorption and desorption. Avrami kinetic model accurately predicted the dynamic CO2 adsorption performance for pristine and metallized biochar.
This novel research is an argumentative subject which was needed to be addressed and to fill this gap, the author examined the effect of financial development, information and communication technology, and institutional quality on CO2 emission in Pakistan by using quantile autoregressive distributed lag (QARDL) model. The data were obtained for the period from 1995Q1 to 2018Q4. In the long run, GDP and institutional quality have a positive impact on CO2 emission when this emission is already high, which shows that if the GDP and institutional quality increases, the CO2 emission also increases. Moreover, financial development and ICT has a negative impact on CO2 emission irrespective of emission level that whether it is high or low in the country, which shows that if financial enhancement and ICT increases, carbon emission decreases. The study also supported the EKC hypothesis in Pakistan.
This paper uses the quantile autoregressive distributed lag (QARDL) model to analyze the impact of economic growth, tourism, transportation, and globalization on carbon dioxide (CO2) emissions in the Malaysian economy. The QARDL model is employed utilizing quarterly data from 1995Q1 to 2018Q4. The results demonstrate that economic growth is significantly positive with CO2 emissions at lower to upper quantiles. Interestingly, tourism has a negative effect on CO2 emissions at higher quantiles. Moreover, globalization and transportation services are positive, with CO2 emissions at upper-middle to higher quantiles. Furthermore, we tested the environmental Kuznets curve, and the outcomes confirm the presence of the inverted U-shaped curve in the Malaysian economy. The results of this study suggest that ecotourism is beneficial for economic growth in underdeveloped areas; it increases employment opportunities and, thus, achieves a win-win situation for protection and development. The government should encourage the low-carbon development of ecotourism and achieve green development of both tourism and the economy.
This study examines the association between transportation services (i.e., passenger and freight) and carbon emissions concerning the US economy. The monthly data for this study were collected for the period from 2000 M1 to 2019 M8. In this study, QARDL econometric approach as discussed by Cho et al. (2015) has been used to tests the relationship between transportation services and CO2 emissions. Due to the chaotic and nonlinear behavior of our concerning variables, it was quite difficult to gauge the principle properties of their variations. Therefore, we relied on QARDL, which has been missing in previous researches. By utilizing the QARDL method, this research assesses the long-term stability of the nexus across the quantiles to provide an econometric framework that is more flexible than the traditional ones. In particular, the authors have analyzed how the quantiles of transportation (i.e., passenger and freight) influence the quantiles of CO2 emissions (environmental degradation). The empirical evidence revealed the negative significant relationship of both the transportation system (i.e., passenger and freight) with carbon emissions; however, this relationship holds at low quantiles of freight transport, whereas the same relationship has been observed at the majority of quantiles of passenger transport. So, this depicts that the transportation system of the USA helps to reduce CO2 emissions. Therefore, to maintain this situation, the government shall introduce more technologies that are fuel-efficient and promote clean consumption, thus reducing CO2 emissions, boosting economic growth, and making green transportation services.
The rise of urbanisation in Belt and Road Initiative (BRI) countries that contribute to the disruption of the ecosystem, which would affect global sustainability, is a pressing concern. This study provides new evidence of the impact of urbanisation and institutional quality on greenhouse gas (GHG) emissions in the selected 48 BRI countries from the years 1984 to 2017. The models of this study are inferred by using panel regression model and panel quantile regression model to meet the objectives of our study as it contemplates unobserved country heterogeneity. From the panel regression model, the findings indicate that although urbanisation in BRI supports the 'life effect' hypothesis that could dampen the environment quality, this effect could be reduced through better institutional quality. Using the quantile regression method, this study concludes that one-size-fits-all strategies to reduce GHG emissions in countries with different GHG emissions levels are improbable to achieve success for all. Hence, GHG emissions control procedures should be adjusted differently across high-emission, middle-emission and low-emission countries. Based on these results, this study provides novel intuitions for policymakers to wisely plan the urbanisation blueprints to eradicate unplanned urbanisation and improve institutional quality in meeting pollution mitigation goals.
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.
The Sustainable Development Goal (SDG) 10 focuses on combating the climate change and its effects. The inclusion of this agenda in the Sustainable Development Goals by the United Nations has shown that worsened environmental degradation is currently a major threat facing humankind. The World Commission on Environment and Development 2015 has highlighted that income inequality is one of the major causes for environmental deterioration. Hence, reducing environmental degradation requires a look at the problem of unequal income distribution. Moreover, educational attainment plays a vital role in providing relevant knowledge and skills to people in handling environmental problems. Thus, the objective of the study is to investigate the relationship between income inequality, educational attainment, and CO2 emissions by employing a panel data analysis for a group of 64 countries from 1990 to 2016.The study uses mainly dynamic common correlated effects (DCCE) estimator to take into account the issue of cross-section dependence which has been ignored by most of the previous studies. By tackling the problem of cross-section dependence, unbiased and reliable results could be produced in estimations. Our results portray that an inverted U-shaped environmental Kuznets curve (EKC) is found to be valid. Additionally, income inequality has a negative impact on environmental degradation. Likewise, educational attainment and CO2 emissions are revealed to be negatively correlated. The findings of the study could provide a better understanding on the root causes of environmental degradation, and further suggest remedial actions to overcome the problem.
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.
Over the last three decades, the world has been facing the phenomenon of the ecological deficit as the ecological footprint is continuously rising due to the persistent decline of the per-capita bio-capacity. Moreover, there is a substantial increase in globalization and electricity consumption for the same period, and transportation is contributing to economic prosperity at the cost of environmental sustainability. Understanding the determinants of ecological footprint is thus critical for suggesting appropriate policies for environmental sustainability. As a result, this study analyzes the impacts of economic globalization, transportation, coal rents, and electricity consumption in ecological footprint in the context of the USA over the period 1995 to 2018. The data have been extracted from "Global Footprint Network," "Swiss Economic Institute," and "World Development Indicators." The current study has also applied the flexible Fourier form nonlinear unit root test to examine the stationarity among variables. For the empirical estimation, a novel technique, the "quantile auto-regressive distributive lag model," is applied in the study to deal with the nonlinear associations of the variables and to evaluate the long-term stability of variables across quantiles. The study's findings indicate that coal rents, transportation, and globalization significantly and positively contribute to the deterioration of ecological footprints at different quantile ranges in the short and long run. Electricity consumption is found to have a positive and significant impact at lower quantile ranges in the long run but not have a significant impact in the short run. The study suggested that lowering the dependence of the transport sector on fossil fuels, more use of hydroelectricity, and stringent strategies to curb coal consumption would be helpful to reduce the positive influence of these variables on ecological footprints in the USA.
Financial development is identified as one of the significant factors that affect energy consumption and has been widely discussed in the literature. However, the association between financial development and renewable energy consumption is still at its earlier stage and is limitedly explored. Therefore, the purpose of this study is to examine the non-linear association between financial development and renewable energy consumption in the top renewable energy consumption countries. The study utilized the newly introduced econometric technique panel smooth transition regression (PSTR) model with two regimes on annual panel data consisted of years 1997-2017. The result confirmed that all the financial development indicators increase renewable energy consumption but affect renewable energy consumption differently. Moreover, the economic growth and industrial structure showed a positive and significant association in both regimes, whereas the population showed a negative relationship with renewable energy consumption in a low growth regime but the association becomes positive in high growth regimes. The study suggested several policies for the top renewable consumption countries.
The connection between ecological footprint and economic complexity has significant implications for environmental sustainability regarding the policy. Additionally, institutional quality is crucial in ensuring environmental sustainability and moderating the link between economic complexity and ecological footprint. The task of achieving sustainable environmental development and preventing further degradation of the environment poses a formidable challenge to policymakers. This study delves into the significance of technology innovation and renewable energy in creating a more sustainable environment. Recognizing the need for a more critical review, this research establishes the dynamic linkage between ecological footprint, renewable energy consumption, and technological innovation, especially in conjunction with a moderating component, particularly institutional quality, in G20 countries from 1990 to 2021. We employ advanced panel approaches to address panel data analysis concerns, such as cross-sectional dependence, slope heterogeneity, unit root, cointegration test and CS-ARDL. The long-term estimator indicates that renewable energy and technological innovation negatively but significantly impact the ecological footprint. Whilst economic growth, FDI, and urbanization have shown a positive and significant impact on ecological footprint; institutional quality negatively moderates the relationship between ecological footprint, renewable energy, and technological innovation in the G20 countries. Further evidence from the Dumitrescu-Hurlin Granger causality test shows that efforts to expand access to renewable energy, technological advancements, and economic growth will significantly affect environmental impacts. Based on our results, it is imperative to introduce more favorable legislation and encourage technological advancements in the field of renewable energy if we want to achieve our sustainable development objectives.
With the growing nature of the ecological footprint, research studies focus on exploring new determinants of environmental degradation. Moreover, the role of natural resources and energy consumption in environmental quality has gained much attention in the literature. However, tourism raises the demand for energy consumption and extraction of natural resources. This research study investigates the influence of natural resources, tourism, and renewable energy in MINT countries, using novel Cross-Sectional Auto Regressive Distributive Lag (CS-ARDL) methodological techniques and employing yearly data from 1995 to 2018. The study also applied recently developed Kónya (Econ Model 23:978-992, 2006) causality to identify the causal relationship between the variables of the heterogenous panel. The result shows that tourism, natural resources, and economic growth are positively associated with the ecological footprint in the long-run. However, renewable energy consumption negatively impacts ecological footprint in both in short-run and the long-run. Further, the study explored a bidirectional causality between economic growth and ecological footprint in MINT countries. Finally, based on the empirical results, the study recommends that the authorities in MINT countries revisit their tourism, natural resources, and economic activities policies to enhance the environmental quality and reduce the ecological footprint.
Policy adjustments can help strike a balance between economic growth and environmental sustainability, which has increasingly been the heart to nations and regions throughout the World. This paper examines how public investment affects economic growth, energy consumption, and CO2 emissions in eight ASEAN countries: Cambodia, Myanmar, Malaysia, Indonesia, the Philippines, Singapore, Thailand, and Vietnam. Extension of a Cobb-Douglas production function and application of panel cointegration techniques reveal bidirectional Granger causation between public investment and both private development and CO2 emissions from 1980 to 2019. Public investment Granger causes energy usage, the opposite does not hold statistically. More findings from pooled mean group estimations show a mean-reversion dynamic that corrects disequilibria by 14% yearly. State investment crowds in private sector growth, energy use, and carbon footprint. It also finds an inverted U-shaped relationship between public investment and energy consumption, and a U-shaped relationship between public investment and CO2 emissions, indicating complex regional interactions. It is suggested the implementation of public investment policies that enrich green infrastructure projects to foster growth while minimizing environmental impacts, and encourage a strategic approach to public investment for prioritizing environmental sustainability and thus, achieving Sustainable Development Goals 7 to 9 and 11 to 13 in this region.
The practice of aquaculture is associated with the generation of a substantial quantity of effluent. Microalgae must effectively assimilate nitrogen and phosphorus from their surrounding environment for growth. This study modeled the algal biomass film, NO3-N concentration, and pH in the membrane bioreactor using the response surface methodology (RSM) and an artificial neural network (ANN). Furthermore, it was suggested that the optimal condition for each parameter be determined. The results of ANN modeling showed that ANN with a structure of 5-3 and employing the transfer functions tansig-logsig demonstrated the highest level of accuracy. This was evidenced by the obtained values of coefficient (R2) = 0.998, R = 0.999, mean squared error (MAE) = 0.0856, and mean square error (MSE) = 0.143. The ANN model, characterized by a 5-5 structure and employing the tansig-logsig transfer function, demonstrates superior accuracy when predicting the concentration of NO3-N and pH. This is evidenced by the high values of R2 (0.996), R (0.998), MAE (0.00162), and MSE (0.0262). The RSM was afterward employed to maximize the performance of algal film biomass, pH levels, and NO3-N concentrations. The optimal conditions for the algal biomass film were a concentration of 2.884 mg/L and a duration of 6.589 days. Similarly, the most favorable conditions for the NO3-N concentration and pH were 2.984 mg/L and 6.787 days, respectively. Therefore, this research uses non-dominated sorting genetic algorithm II (NSGA II) to find the optimal NO3-N concentration, algal biomass film, and pH for product or process quality. The region has the greatest alkaline pH and lowest NO3-N content.