Implications For CO2 Emissions Research Articles

Dampak Pertumbuhan Ekonomi, Urbanisasi Dan Konsumsi Energi Listrik Terhadap Degradasi Lingkungan: Bukti dari Negara Asean-5

This study aims to analyze the impact of economic growth, urbanization, and electrical energy consumption on environmental degradation in ASEAN 5 countries. They use a quantitative approach and annual panel data for 1992-2021. The object of research is ASEAN-5 countries, including Indonesia, Thailand, Malaysia, Singapore, and the Philippines. The analysis technique uses the Panel ARDL method. The findings prove that economic growth and electrical energy consumption, in the long run, impact CO2 emissions, and urbanization has (-)* implications for CO2 emissions. Meanwhile, economic growth variables in the short term affect (-) CO2 emissions. Urbanization and electrical energy consumption variables have a (+) impact on CO2 emissions.

Energy Subsidies and their Implications for CO2 Emissions in the Visegrad Group Countries

The aim of the article is to present a review of the literature on energy subsidies and the scale and structure of subsidies for energy production in the Visegrad Group countries. It also presents the most important results of an investigation into the relationship and impact of fossil fuel subsidies on CO2 emissions based on a linear regression model. Due to the availability of comparable statistical data, the survey was limited to the period 2015–2020. The analysis does not provide a clear confirmation of the negative impact of the amount of subsidies (from the current or previous year) on the level of CO2 emissions.

The environmental cost of FDI and spatial implications of CO2 emissions in Sub-Saharan Africa.

This study endeavors to investigate the environmental cost of FDI inflows in the Sub-Saharan African (SSA) region over the period of 2006 to 2020. There are two opposing theories about how FDI impacts the environment, namely, the pollution halo hypothesis (PHH) and the pollution haven hypothesis (PH). Given the SSA region's poor environmental performance and potential spatial spillover effects on neighboring nations, the study underlines the necessity to look into the pollution hypotheses in theregion. The examination is carried out through non-spatial and spatial panel data econometric approaches. The empirical findings provide evidence that an increase in FDI inflow by 1% in SSA is positively associated with increasing levels of CO2 emissions by 0.03% on average, thus validating the notion of a pollution haven in the region. Furthermore, the study reveals that the environmental spillovers of CO2 emissions are not confined to the host country alone, but also extend to neighboring nations. Other key determinants of CO2 emissions, including GDP, population, and urbanization, were also found to be positively linked to CO2 emissions, whereas the use of renewable energy resources was found to have a mitigating effect. The empirical findings offer valuable insights for policymakers and stakeholders in the SSA region. These insights highlight the importance of the adoption of renewable energy sources and enacting regulatory measures to monitor the environmental cost of FDI, with the aim of mitigating the deleterious consequences of CO2 emissions, not only in the host nation but also in the neighboring nations.

Implications of CO2 emissions on the main land and forest uses in the Brazilian Amazon

The emission of soil carbon dioxide (CO2) in agricultural areas is a process that results from the interaction of several factors such as climate, soil, and land management practices. Agricultural practices directly affect the carbon dynamics between the soil and atmosphere. Herein, we evaluated the temporal variability (2020/2021 crop season) of soil CO2 emissions and its relationship with related variables, such as the CO2 flux model, enhanced vegetation index (EVI), gross primary productivity (GPP), and leaf area index (LAI) from orbital data and soil temperature, soil moisture, and soil CO2 emissions from in situ collections from native forests, productive pastures, degraded pastures, and areas of high-yield potential soybean and low-yield potential soybean production. A significant influence (p < 0.01) was observed for all variables and between the different land uses and occupation types. September and October had lower emissions of soil CO2 and low means of soil moisture and soil temperature, and no differences were observed among the treatments. On the other hand, there was a significant effect of the CO2 flux model in productive pastures, high-yield potential soybean areas, and low-yield potential soybean areas. The months with the highest CO2 flux values in the model, regardless of land use and land cover, were October and November, which is the beginning of the rainy season. There were positive correlations between soil CO2 emissions and GPP (0.208), LAI (0.354), EVI (0.363), and soil moisture (0.280) and negative correlations between soil CO2 emissions and soil temperature (−0.240) and CO2 flux model (−0.314) values. Land use and land cover showed negative correlations with these variables, except for the CO2 flux model variable. Soil CO2 emission values were lower for high-yield potential soybean areas (averages from 0.834 to 6.835 μmol m−2 s−1) and low-yield potential soybean areas (from 0.943 to 5.686 μmol m−2 s−1) and higher for native forests (from 2.279 to 8.131 μmol m−2 s−1), whereas the opposite was true for the CO2 flux model.

Driving factors and emission reduction scenarios analysis of CO2 emissions in Guangdong-Hong Kong-Macao Greater Bay Area and surrounding cities based on LMDI and system dynamics

As one of the most open and economically dynamic regions in China, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is at the forefront of low-carbon development and has an exemplary and leading role for other regions. This study provides a research framework based on the Logarithmic Mean Divisia Index (LMDI) and system dynamics (SD) by first compiling an inventory of CO2 emissions in the GBA and surrounding cities from 2000 to 2019 and then systematically and comprehensively analyzing the driving factors, future trends and policy implications of CO2 emissions in the GBA and surrounding cities. The results show that (a) CO2 emissions in the GBA and surrounding cities grew from 253.39 Mt in 2000 to 627.86 Mt in 2019, with an average annual growth rate of 4.89 %. The per capita CO2 emissions showed a continuous decreasing trend, and the overall carbon intensity of each sector showed a decreasing trend. (b) GDP per capita growth has the greatest effect on CO2 emissions, followed by the number of transport vehicles and population. The negative effects are energy intensity, average output of transportation vehicles, and residential energy intensity, with energy intensity being the most critical. (c) In the baseline scenario, regional CO2 emissions in 2030 are 1.25 times higher than those in 2019 and continue to grow. (d) Technological innovation measures are the most effective among individual emission reduction policies, followed by optimization of industrial structure. Furthermore, energy structure adjustment, vehicle licensing restrictions, and residents' green living are less effective. (e) Under comprehensive emission reduction measures, the region can achieve carbon emissions peaking in 2026 and reduce the regional carbon intensity by 66.24 % in 2030 compared with 2005. This study provides effective data support for the GBA and surrounding cities to formulate low carbon policies, promote carbon emission reduction and achieve carbon emissions peaking early.

The Nexus Between Fiscal Decentralization and Environmental Sustainability in Japan

This paper adds to the existing body of knowledge by incorporating the role of fiscal decentralization (FD) in influencing CO2 emissions. Therefore, this study looked at the effect of FD on CO2 emissions in the presence of nonrenewable energy consumption (NRE), renewable energy consumption (REN), gross domestic product (GDP), and trade openness (TOP) for the period 1994–2018 in Japan. Thus, the current work intends to fill this knowledge gap by employing econometric techniques such as Bayer and Hanck cointegration, dynamic ordinary least squares (DOLS), fully modified ordinary least squares (FMOLS), and canonical cointegration regression (CCR). Additionally, the frequency domain causality analysis is used in the investigation to determine the causal impact of FD, NRE, REN, GDP, and TOP on CO2 emissions. The novelty of the frequency-domain approach is that it can differentiate between nonlinearity and causality levels and show causality among parameters with different frequencies. The DOLS, FMOLS, and CCR results reveal that NRE, GDP, and TOP augment CO2 emissions in Japan, whereas FD and REN increase the quality of the atmosphere. Furthermore, the frequency causality test results show that FD, REN, GDP, and TOP have implications for CO2 emissions in the long run, while NRE raises CO2 emissions in the medium run. As a policy direction, the current study suggests expanding renewable energy consumption in Japan by emphasizing more on Sustainable Development Goals (7, 8, and 13).

Multi-Objective Fuzzy Tourist Trip Design Problem with Heterogeneous Preferences and Sustainable Itineraries

Tourism has direct and indirect implications for CO2 emissions. Therefore, it is necessary to develop tourism management based on sustainable tourism, mainly in the transport process. Tourist itinerary planning is a complex process that plays a crucial role in tourist management. This type of problem, called the tourist trip design problem, aims to build personalised itineraries. However, planning tends to be biased towards group travel with heterogeneous preferences. Additionally, much of the information needed for planning is vague and imprecise. In this paper, a new model for tourist route planning is developed to minimise CO2 emissions from transportation and generate an equitable profit for tourists. In addition, the model also plans group routes with heterogeneous preferences, selects transport modes, and addresses uncertainty from fuzzy optimisation. A set of numerical tests was carried out with theoretical and real-world instances. The experimentation develops different scenarios to compare the results obtained by the model and analyse the relationship between the objectives. The results demonstrate the influence of the objectives on the solutions, the direct and inverse relationships between objectives, and the fuzzy nature of the problem.

Financial Markets and Carbon Dioxide Emissions

This report considers the impact of carbon dioxide (CO2) emissions on financial markets. The following questions are addressed: • Should CO2 emissions impact stock returns? • Do CO2 emissions impact stock returns? • Should CO2 emissions impact bond yields and do they? • What are the implications of CO2 emissions for asset management?

Impacts of COVID-19 and fiscal stimuli on global emissions and the Paris Agreement

The global economy is facing a serious recession due to COVID-19, with implications for CO2 emissions. Here, using a global adaptive multiregional input–output model and scenarios of lockdown and fiscal counter measures, we show that global emissions from economic sectors will decrease by 3.9 to 5.6% in 5 years (2020 to 2024) compared with a no-pandemic baseline scenario (business as usual for economic growth and carbon intensity decline). Global economic interdependency via supply chains means that blocking one country’s economic activities causes the emissions of other countries to decrease even without lockdown policies. Supply-chain effects contributed 90.1% of emissions decline from power production in 2020 but only 13.6% of transport sector reductions. Simulations of follow-up fiscal stimuli in 41 major countries increase global 5-yr emissions by −6.6 to 23.2 Gt (−4.7 to 16.4%), depending on the strength and structure of incentives. Therefore, smart policy is needed to turn pandemic-related emission declines into firm climate action. Global emissions could decrease 3.9–5.6% over 5 years due to COVID-19, and the interconnected economy means lockdown-related declines reach beyond borders. As countries look to stimulate their economies, how fiscal incentives are allocated and invested will determine longer-term emission changes.

A Gini approach to spatial CO2 emissions.

Combining global gridded population and fossil fuel based CO2 emission data at 1 km scale, we investigate the spatial origin of CO2 emissions in relation to the population distribution within countries. We depict the correlations between these two datasets by a quasi-Lorenz curve which enables us to discern the individual contributions of densely and sparsely populated regions to the national CO2 emissions. We observe pronounced country-specific characteristics and quantify them using an indicator resembling the Gini-index. As demonstrated by a robustness test, the Gini-index for each country arise from a compound distribution between the population and emissions which differs among countries. Relating these indices with the degree of socio-economic development measured by per capita Gross Domestic Product (GDP) at purchase power parity, we find a strong negative correlation between the two quantities with a Pearson correlation coefficient of -0.71. More specifically, this implies that in developing countries locations with large population tend to emit relatively more CO2, and in developed countries the opposite tends to be the case. Based on the relation to urban scaling, we discuss the implications for CO2 emissions from cities. Our results show that general statements with regard to the (in)efficiency of large cities should be avoided as it is subject to the socio-economic development of respective countries. Concerning the political relevance, our results suggest a differentiated spatial prioritization in deploying climate change mitigation measures in cities for developed and developing countries.

Role of economic structural change in the peaking of China's CO2 emissions: An input–output optimization model

To combat climate change, the Chinese government has announced that the country will reach its national carbon emission peak within 2030. Various scenario studies suggest that technological advances in energy, such as energy efficiency and renewables, would be the leading determining factors for the peak in China's carbon emissions. However, most of these studies have failed to reflect the fact that China is shifting its economy from energy-intensive industries to non-energy-intensive industries, which may play a vital role in mitigating carbon emissions. To assess how economic structural changes may contribute to carbon emissions, an input–output optimization model was constructed and scenario analyses were performed. This model introduced the input-output model integrated with an optimization model to ensure the balance of economic structure in an optimal result. The results show that in 2030, China could peak its carbon emissions at 12.41 Gt CO2eq (parts per giga ton; carbon dioxide equivalent) by adjusting its energy-intensive economic structure of which the key sectors are coke refined petroleum and nuclear fuel (C8), chemicals and chemical products (C9), other nonmetallic minerals (C11), basic and fabricated metals (C12), and electricity gas and water supply (C17). The continuous increase in the added value of the tertiary industry could maintain a high GDP growth rate of 5.6% when the secondary industry is restricted to reduce carbon emissions. Accelerating the pace of China's economic transformation will be very conducive to an earlier realization of peaking CO2 emissions because the inhibition effect of structural change on carbon emissions presents an increasing marginal trend. From a policy perspective, the analytical techniques in this study could provide valuable information for decision-makers to regulate sector capital investment and formulate practical industrial policies with implications for CO2 emissions.

Economic performance of India amidst high CO2 emissions

Abstract In recent times, the Indian economy has enjoyed a positive economic trajectory. However, the economy remains vulnerable to domestic and geographical risk as it relates to environmental degradation. The Indian economy is reputed as one of the leading emitters of carbon dioxide ( C O 2 ) emissions globally.. In the literature, there is no consensus on the contradiction between decoupling economic growth from CO2 emission in India and other regions of the globe. By drawing on the Sustainable Development Goals (SDGs) agenda and its 2030 targets, this study examines the relationship between responsible energy consumption (SDGs 7, 12), climate issues (SDGs 13), and economic growth (SDGs 8) for the case of India. Thus, the present study seeks to investigate the implications of CO2 emissions on Indian economic growth (GDP) with a focus on the energy intensity in the country's economy. To explore the nexus between economic growth and environmental degradation in a carbon income function, openness to trade and energy use were added as additional variables to circumvent the problem of omitted variable bias. Autoregressive Distributed Lag (ARDL) with the modified Wald test of Toda Yamamoto (T-Y) were applied to annual time series data from 1975-2017. The study reveals a long-term equilibrium association among the described variables over the considered period. Furthermore, a statistically significant negative relationship is observed between C O 2 emissions and trade openness and economic growth. This study validates the energy-induced economic growth as reported by the ARDL regression. This is also corroborated by the causality analysis results, as a uni-directional relationship was observed running from energy utilization to income (GDP). Thus, the Indian government officials should not adopt conservative energy policies, as this will be detrimental for economic growth given that the economy is dependent on energy. However, based on the growing environmental consciousness around the world, there is a need to shift the energy mix in India to renewables to make uses of cleaner energy sources and create environmentally friendly ecosystems.

Estimating the diffusion of rooftop PVs: A real estate economics perspective

Currently, the economic performance of rooftop photovoltaics (PVs) is predominantly evaluated using the levelized cost of electricity approach. Some researchers have argued that rooftop PVs should be viewed as an investment, where performance is evaluated using relevant investment analysis methods. This article argues that the proper economic value and climate mitigation potential of rooftop PVs can be assessed only if the market mechanisms and characteristics of the underlying building stock are understood better. This paper utilises a customer-driven investment model to examine the feasible market potential of rooftop PVs in an urban environment. The economic returns of PV installations are calculated for 89,000 buildings separately and by comparing them to the underlying spatial property returns, the profitability and adoption years of rooftop PVs for in different locations are estimated. Implications for CO2 emissions and the impact of the net metering (NM) policy on the rate of adoption are calculated. The results imply that even in Finland rooftop solar PVs are already profitable for a third of the residential building stock and will be profitable for a fourth of the commercial building stock by 2025. Finally, the NM could increase substantially the diffusion rate of solar PVs in the coming years.

Sensitivity analysis of China’s energy-related CO2 emissions intensity for 2012 based on input–output Model

This paper employed a sensitivity analysis based on the input-output model to identify the key sectors and main productive linkages between activity branches in terms of CO2 emissions in China. In particular, we established the Chinese energy-input-output table for 2012. Based on the input-output data, sensitivity analysis, is introduced to probe into two major drivers, i.e., the emissions intensity coefficient (c) and technology coefficient (B). The results show that (1) Regarding the driver c, the top six emissions-intensity sectors are tested to be the key sectors which will cause the highest emissions. (2) For B, the emissions intensity is the most sensitive to the technology change of the Production and supply of electric power and heat industry (n4) with direction transaction relation. (3) With respect to the values of two elasticity indicators for B, these sectors with a higher “structure-relevant” have a lower “technology-relevant”. This implied that the technology coefficient has more influence on the CO2 emission intensity of n4 after considering the structural impact of final demand. Emphatically, the analytical method used in this study can provide valuable information for planners and decision makers to formulate feasible and practical industrial policies with implications for CO2 emissions.

Changing industrial structure to reduce carbon dioxide emissions: a Chinese application

Increasing concern about carbon dioxide (CO2) emission reduction demands knowledge about the production structure of an economy. Information on productive linkages yields insight about forward and backward emission effects associated with sectoral inputs and outputs and serves as an essential starting point to identify the optimised industrial structure under the constraint of CO2 emission. This paper proposes a combined linkage analysis and multi-objective programming approach to identify the key CO2 emission sectors and the optimised production structure with respect to emission reduction target. As a demonstration, the proposed approach is applied to data from China in 2007. The result shows that to reduce CO2 emissions from 5707.16 to 5452.12 million tonnes, China needs to change its industrial structure by focussing on industrial groups as defined by linkage characteristics, which would lead to a subsequent GDP decrease of 82.59 billion Yuan. From a policy standpoint, the analytical techniques described in this paper can provide valuable information for planners and decision makers to formulate feasible and practical industrial polices with implications for CO2 emissions.

Global implications of joint fossil fuel subsidy reform and nuclear phase-out: an economic analysis

This paper uses the OECD’s global recursive-dynamic general equilibrium model ENVLinkages to examine the mid-term economic consequences and the optimal energy supply mix adjustments of a simultaneous implementation of i) a progressive fossil fuel subsidy reform in emerging and developing economies and ii) a progressive phase out of nuclear energy, mostly affecting OECD countries, China and Russia. The analysis is then transposed in the context of climate change mitigation to depict the corresponding implications for CO2 emissions, to assess the interactions between the two energy policies, and to derive how the associated costs are affected by the different policies. The phase-out scenario projects a nuclear capacity halved by 2035 as compared to the Baseline, corresponding to $120 billion losses in value-added of the nuclear industry for that year. The nuclear phase-out leaves GDP and real household consumption marginally affected in energy importing countries. A multilateral subsidy reform is more likely to affect international fossil fuel prices and alter patterns of global energy use. The fossil fuel subsidy reform, when implemented together with nuclear phase-out, more than offsets negative consequences on household consumption but still leads to a decrease in global CO2 emissions. The combined policies help save the equivalent of current energy consumption in the Middle East. Combining a climate policy, an effective fossil fuel subsidy reform, even with a lower nuclear share in the power mix, brings about multiple benefits to OECD countries which reduce their energy bill and achieve large climate change mitigation at lower cost.

Applications of Higher-Order Optimal Newton Secant Iterative Methods in Ocean Acidification and Investigation of Long-Run Implications of CO2 Emissions on Alkalinity of Seawater

The Newton secant method is a third-order iterative nonlinear solver. It requires two function and one first derivative evaluations. However, it is not optimal as it does not satisfy the Kung-Traub conjecture. In this work, we derive an optimal fourth-order Newton secant method with the same number of function evaluations using weight functions and we show that it is a member of the King family of fourth-order methods. We also obtain an eighth-order optimal Newton-secant method. We prove the local convergence of the methods. We apply the methods to solve a fourth-order polynomial arising in ocean acidifications and study their dynamics. We use the data of CO2 available from the National Oceanic and Atmospheric Administration from 1959 to 2012 and calculate the pH of the oceans for these years. Finally we further investigate the long-run implications of CO2 emissions on alkalinity of seawater using fully modified ordinary least squares (FMOLS) and dynamic OLS (DOLS). Our findings reveal that a one-percent increase in CO2 emissions will lead to a reduction in seawater alkalinity of 0.85 percent in the long run.

Implications for CO2 emissions and sinks changes in western China during 1995–2008 from atmospheric CO2 at Waliguan

ABSTRACTCarbon dioxide (CO2) emissions and sinks in western China are estimated and implied from atmospheric CO2 measured at Waliguan during the period of 1995–2008. The observed CO2 data are first classified as background, elevated, and sequestration, using a modified background identification method. Comparing it with two other methods tests the applicability of the method. By using this method, approximately 17.2%±1.2% and 10.1%±0.8% of all observed data are identified as elevated and sequestered CO2, respectively, the percentages (occurrence rates) for both of which increased during 1995–2008. CO2 emissions in western China have enhanced significantly in all seasons during the past 14 yr. Annual mean growth rates of CO2 emissions in the region increased by ~8.4 Tg C yr−1 (3.9% yr−1) during 1995–2008 but accelerated after 2000 to ~12.6 TgC yr−1 (6.2% yr−1). The growth rates of CO2 emission in western China are lower than the rest of the country. The annual mean emissions in the country during 1995–1999 and 2000–2006 are thought to be approximately 5.5 and 6.5 times higher than in western China, respectively. However, the growth rates of CO2 emissions in western China are higher than global increase rates as reported by other studies. CO2 sinks in western China varied from 86.0 Tg C yr−1 in early 1995–1999 to 106.2 Tg C yr−1 in 2005–2008. The most prominent change occurs in summer, indicating enhanced sequestration of photosynthetic CO2 taken up by vegetation. The growth rates in sequestered CO2 cannot keep up with the increase of CO2 emissions in the region. If this continues, it could potentially impact on the global carbon budget.

Implications of CO2 emissions trading for short-run electricity market outcomes in northwest Europe

We examine the short-run implications of CO2 trading for power production, prices, emissions, and generator profits in northwest Europe in 2005. Simulation results from a transmission-constrained oligopoly model are compared with theoretical analyses to quantify price increases and windfall profits earned by generators. The analyses indicate that the rates at which CO2 costs are passed through to wholesale prices are affected by market competitiveness, merit order changes, and elasticities of demand and supply. Emissions trading results in large windfall profits, much but not all of which is due to free allocation of allowances. Profits also increase for some generators because their generation mix has low emissions, and so they benefit from electricity price increases. Most emission reductions appear to be due to demand response not generation redispatch.

The Economics of Exhaustible Resources

The discussion is confined in scope to absolutely irreplaceable assets. Topics include peculiar problems of mineral wealth, free competition, maximum social value and state regulation, monopoly, value of a mine monopoly, retardation of production under monopoly, price effects from cumulated production, and the author's mathematically derived optimum solutions. (PCS)