Changes In CO2 Emissions Research Articles

Moving towards a green decoupling between economic development and environmental stress? A new comprehensive approach for Ecuador

ABSTRACT Focused on Ecuador’s Economy this article analyses for the 2000–2014 period. (i) What have the drivers of CO2 emission in Ecuador been? (ii) What are the determining factors to be focused on with regards to energy policy efforts and the mitigation of GHG emissions? And (iii) Is the economy of Ecuador moving towards decoupling between economic growth and environmental stress? To respond to question (i), a decomposition analysis of the change in CO2 emissions has been developed using the LMDI-I model. The decomposing factors or effects used include Carbon Intensity, Renewable Energy Sources penetration, Energy Intensity, Economic Structure, Economic Activity and Population. To answer question (ii) a so-called Innovative Accounting Approach (IAA) has been implemented. Another innovative aspect of the methodology used has been to incorporate decoupling analysis between economic growth and environmental stress. This facilitates answering question (iii) which is the first time that a comprehensive model is undertaken as described. The methodological approach of the paper linking decoupling analysis with an ex-post approach (LMDI) and with an ex-ante one (IAA) makes its contribution going beyond the specifics of the case of Ecuador. Additionally, Literature focused on Ecuador is scarce when compared with other countries size similar.

Spatial decomposition of city-level CO2 emission changes in Beijing-Tianjin-Hebei

China is the world’s largest carbon emitter, and the Beijing-Tianjin-Hebei metropolitan region is an energy-intensive area in China. Under the requirements of the national policy of regional green development, the Beijing-Tianjin-Hebei region needs to shift to a low-carbon development mode to achieve regional emission reduction targets. The varying characteristics of industrial carbon emissions resulting from differences in the industrial development and resource endowment of each city make it difficult to implement the same emission reduction policy, so different cities need to be treated differently. Based on data from three provinces and eight cities in the region from 2002 to 2016, this study used the multi-regional (M-R) spatial decomposition method to compare the carbon emissions and the effects of their influencing factors in each city, so as to provide a targeted emission reduction policy for each city. The estimation results revealed that carbon emissions in the Beijing-Tianjin-Hebei region peaked in 2012, and energy intensity and population size are the main reasons for the differences in carbon emissions at the provincial level. At the city level, Tangshan, Handan and Shijiazhuang are high carbon emission cities, while Hengshui, Baoding, Langfang, Qinhuangdao and Xingtai are low carbon emission cities. The total amount of carbon emission in all industries in Tangshan ranks the first, and economic scale and energy intensity are the main reasons for the high carbon emissions in its manufacturing and transportation sectors. Energy intensity and energy structure are the main reason why the carbon emission of Handan is higher than that of Baoding, a city with the same economic level. Shijiazhuang has high energy efficiency, but its economic development and carbon emissions have not yet been decoupled. Hengshui’s high energy intensity is an obstacle to emission reductions in all industries. Xingtai’s energy structure contributes to carbon emission reduction. The current industrial structures of Qinhuangdao, Langfang, and Shijiazhuang may help these cities reduce emissions. Lastly, we offer recommendations for urban emission reduction policies.

Unhiding the role of CHP in power & heat sector decomposition analyses

In many countries the role of combined heat & power (CHP) generation in the power & heat sector is significant. However, in decomposition analyses of the power & heat sector the contribution of CHP to observed changes in primary energy use or CO2 emissions is generally not made explicit. In this paper, the contribution of CHP is shown for eight countries (China, Denmark, France, Germany, Italy, the Netherlands, Poland and the USA) in the period 2005–2016. In addition, an alternative method is proposed for power & heat sector decomposition analysis with five driving factors: volume effect, subsector effect, heat effect, fuel mix effect and efficiency effect. This method combines indicators from existing decomposition methods and complements them with a CHP specific heat effect. The proposed method provides improved insight in the factors driving change in primary energy use or CO2 emissions in the power and heat sector, especially in case changes take place regarding either 1) the power-to-heat ratio, 2) the share of CHP electricity in total electricity production, 3) the CHP fuel mix, and/or 4) the efficiency of individual CHP fuels.

Using Logarithmic Mean Divisia Index method (LMDI) to estimate drivers to final energy consumption and emissions in ASEAN

The ASEAN is currently trying to mitigate the amount of CO2 emissions per country. This is not only to contribute to the worldwide goal of lessening greenhouse gases, but to also ensure the safety of its member states by combatting the effects of climate change. The effects as stated by the IPCC, such as rising water levels, rising ambient temperature, and an increase in the frequency of tropical storms, can greatly endanger members of the ASEAN. It must be noted that a majority of its members are archipelagic, have areas of extreme poverty, and rely on agriculture for production. Therefore, the temporal logarithmic mean Divisia index (LMDI) has been conducted in order to determine the possible drivers that cause the changes in CO2 emissions per year for all ASEAN member countries. The drivers of CO2 were divided into the population, economic activity, energy intensity, and energy structure effects. CO2 emissions per fuel type were calculated using their respective emission factors. Knowing which drivers constitute the most CO2 emissions in a country would help pinpoint which areas could be worked on. It was found that Indonesia, Brunei Darussalam, and Lao PDR had the most impressive decrease in CO2 emissions due to energy intensity and structure effects. A majority of the other countries have either stayed constant or even increased CO2 emission output per year from each effect. Therefore, general policies based on the success of these countries that could be applied for each member country are recommended in this study.

Environmental analysis of Turkey's aggregated and sector-level CO2 emissions.

Turkey's ecological footprint has recently been increasing, while its biocapacity has been decreasing. Furthermore, the country's CO2 emissions have been rising substantially in recent decades. Therefore, this study aims to identify the factors that are changing the CO2 emissions in Turkey, not only at the aggregated level but also for the sectors with the highest increases in CO2 emissions, namely, electricity and heat production. Research into the aggregated CO2 emissions and the sector-level analysis covered the period 1990-2017. The Shapley decomposition method was utilized, and the impacts of five factors, namely, scale effect, migration effect, population, energy intensity, and carbon intensity, were considered to examine the changes in total CO2 emissions. Regarding the analysis of the electricity and heat production sectors, the roles of four factors, namely, income effect, electricity intensity, fuel structure, and pollution coefficient, were investigated by using the same decomposition approach. The results showed that the scale effect is the primary driver of acceleration of total CO2 emissions in Turkey; population, migration, and carbon intensity effects follow the scale effect in this respect. Energy intensity significantly decreases Turkey's total emissions. For the electricity and heat production sectors, the Shapley decomposition results showed that the income effect, electricity intensity, and fuel structure increase the country's CO2 emissions rapidly. The pollution coefficient was the only factor that decreased CO2 in these sectors in the studied period. This study reveals that Turkey is still deficient in terms of green development processes, which are essential for a sustainable future.

Eco-friendly and economically optimal design model (EEODM) to reduce the CO2 emissions and the cost of long-span waffle slabs

As the emission of CO2 is directly proportional to the nature of the materials used in the construction of buildings, an eco-friendly economic design is required for waffle slabs used in large buildings such as stores, shopping malls. In this study, a multi-objective optimization design model, denoted as an eco-friendly and economically optimal design model (EEODM), is proposed to reduce the CO2 emissions and costs of long-span waffle slabs. The developed model was analyzed to determine the change in CO2 emissions and costs with respect to design variables in 20 types of long-span waffle slabs. Based on the results of the analysis, CO2 emission efficiency analysis (CEEA) and cost-efficiency analysis (CEA) are proposed as the new qualitative scales to determine environmental and economic feasibility, respectively. Through CEEA and CEA, a new design standard is proposed to overcome the limitations of existing waffle forms. To verify the applicability of the EEODM, the model proposed in this study was applied to the actual waffle slab of Gimpo International Airport in Korea. The results indicate that the two proposed waffle forms reduced CO2 emissions and costs compared to the existing sections of the airport.

Does Forest Industries in China Become Cleaner? A Prospective of Embodied Carbon Emission

Forests and the forest products industry contribute to climate change mitigation by sequestering carbon from the atmosphere and storing it in biomass, and by fabricating products that substitute other, more greenhouse-gas-emission-intensive materials and energy. This study investigates primary wood-working industries (panel, furniture, pulp and paper) in order to determine the development of carbon emissions in China during the last two decades. The input–output approach is used and the factors driving the changes in CO2 emissions are analyzed by Index Decomposition Analysis–Log Mean Divisia Index (LMDI). The results show that carbon emissions in forest product industries have been declining during the last twenty years and that the driving factor of this change is the energy intensity of production and economic input, which have changed dramatically.

Modeling the Effects of Agricultural Innovation and Biocapacity on Carbon Dioxide Emissions in an Agrarian-Based Economy: Evidence From the Dynamic ARDL Simulations

In this paper, we modeled the effects of income, agricultural innovation, energy utilization, and biocapacity on Carbon dioxide (CO2) emissions. We tested the validity of the environmental Kuznets curve (EKC) hypothesis for Nigeria from 1981 to 2014. We applied the novel dynamic autoregressive distributed lag (ARDL) simulations to develop conceptual tools for policy formulation. The empirical results confirmed the EKC hypothesis and found that agricultural innovation and energy utilization have an escalation effect on CO2 emissions whereas income and biocapacity have long-run emission-reduction effects. The causality results found agricultural innovation attributable to CO2 emissions and observed that income drives energy demand. Income, biocapacity, and energy utilization are found to predict changes in CO2 emissions. These results are validated by the innovation accounting techniques—wherein 22.79% of agricultural innovation corresponds to 49.43% CO2 emissions—5.95% of biocapacity has 35.78% attributable CO2 emissions—and 1.61% of energy spurs CO2 emissions by 16.27%. The policy implication for this study is that energy efficiency, clean energy utilization and sustainable ecosystem recovery and management are the surest ways to combat climate change and its impacts.

The spatial spillover effect of fossil fuel energy trade on CO2 emissions

We diagnose CO2 emission changes and determine the driving mechanisms and spatial spillover effect worldwide using spatial econometric techniques embedded within energy trade in the period 2000–2014. We focus on fossil fuel import, taking it as the medium through which to examine the spatial spillover effect on CO2 emissions, and compare the spatial influence between developed and developing countries. We propose different hypotheses considering the magnitude of spatial influence through fossil fuel trade between developed countries, between developing countries, and between developed and developing countries. These hypotheses are manifested in the multiple spatial econometric model. Results revealed general heterogeneity of CO2 emissions among different countries alongside fluctuations and wavy increments in the analyzed groups of countries. However, the convergence of emissions was predictable because the growth rate of CO2 emissions was low in countries with high levels of CO2 emission and converged rapidly with that of developed countries. The spatial autocorrelation phenomenon and spatial spillover effects generated from energy trade have previously only been verified worldwide rather than solely in developed countries or developing countries. Urbanization, industrial development, deforestation and GDP growth all drive the increase in CO2 emissions whereas renewable energy options can help to mitigate emission increases.

Investigating the Causal Linkage Among Economic Growth, Energy Consumption and CO2 Emissions in Thailand: An Application of the Wavelet Coherence Approach

The study aims to explore the causal linkage between CO2 emissions, economic growth and energy consumption in Thailand utilizing the wavelet coherence approach, conventional Granger and the Toda-Yamamoto causality techniques. In this study, In this study, time-series data spanning the period between 1971 and 2018 were used. No prior study has used the wavelet coherence approach to collect information on the association and causal interrelationship among these economic variables at different frequencies and timeframes in Thailand. The study objectives are structured to answer the following question: Does economic growth and energy consumption lead to CO2 emissions in Thailand?. The findings revealed that: (a) Changes in economic growth led to changes in CO2 emissions in Thailand at different frequencies (different scales) between 1971 and 2018. (b) A bidirectional causal relationship between CO2 emissions and energy consumption. (c) A positive correlation between CO2 emissions and energy usage in the short and long-run between 1971 and 2018. (d) A positive correlation between GDP growth and CO2 emissions in the short and long-run between 1971 and 2018. The study suggested that Thailand should initiate stronger policies towards enhancing the efficiency of energy and energy-usage programs to minimize unnecessary energy waste.

Impacts of oil price uncertainty on energy efficiency, economy, and environment of Malaysia: stochastic approach and CGE model

Uncertainty in global oil prices significantly influences the economic performance of Malaysia as a net oil–exporting country. This study uses an integrated approach, in which a stochastic method is integrated with a computable general equilibrium (CGE) model to examine the impacts of likely change in global oil price on energy efficiency and, consequently, on key economic, energy, and environmental variables of Malaysia. The stochastic method, which is related to the Monte Carlo assessment, is based on historical data of global oil price, during 1980–2017, provides probable changes in oil price and their probability of occurrence. Simulation results show that likely changes in global oil price, with a 90% probability, change energy efficiency in Malaysia between − 0.08 and + 0.06% within which the economic performance of Malaysia changes between − 5.22 and 3.00% and household welfare changes between − 4.81 and 2.92%. Furthermore, the energy demand changes between 1.51 and − 2.93% and CO2 emission changes between 4.21 and − 2.03%. However, the emission of other air pollutants changes between − 2.45 and 2.21%. These economic and environmental changes generate a double dividend effect on the Malaysian economy. The value of the rebound effect also changes between 103.21 and 95.79%. Therefore, the paper highlights a strong interconnection among oil price fluctuation, energy efficiency, energy consumption, CO2 emission, and economic growth and thus the necessity for an integrated policymaking method.

The climate change mitigation impacts of active travel: Evidence from a longitudinal panel study in seven European cities

Active travel (walking or cycling for transport) is considered the most sustainable and low carbon form of getting from A to B. Yet the net effects of changes in active travel on changes in mobility-related CO2 emissions are complex and under-researched. Here we collected longitudinal data on daily travel behavior, journey purpose, as well as personal and geospatial characteristics in seven European cities and derived mobility-related lifecycle CO2 emissions over time and space. Statistical modelling of longitudinal panel (n = 1849) data was performed to assess how changes in active travel, the ‘main mode’ of daily travel, and cycling frequency influenced changes in mobility-related lifecycle CO2 emissions.We found that changes in active travel have significant lifecycle carbon emissions benefits, even in European urban contexts with already high walking and cycling shares. An increase in cycling or walking consistently and independently decreased mobility-related lifecycle CO2 emissions, suggesting that active travel substituted for motorized travel – i.e. the increase was not just additional (induced) travel over and above motorized travel. To illustrate this, an average person cycling 1 trip/day more and driving 1 trip/day less for 200 days a year would decrease mobility-related lifecycle CO2 emissions by about 0.5 tonnes over a year, representing a substantial share of average per capita CO2 emissions from transport. The largest benefits from shifts from car to active travel were for business purposes, followed by social and recreational trips, and commuting to work or place of education. Changes to commuting emissions were more pronounced for those who were younger, lived closer to work and further to a public transport station.Even if not all car trips could be substituted by active travel the potential for decreasing emissions is considerable and significant. The study gives policy and practice the empirical evidence needed to assess climate change mitigation impacts of urban transport measures and interventions aimed at mode shift to more sustainable modes of transport. Investing in and promoting active travel whilst ‘demoting’ private car ownership and use should be a cornerstone of strategies to meet ‘net zero’ carbon targets, particularly in urban areas, while also reducing inequalities and improving public health and quality of urban life in a post-COVID-19 world.

Changes in soil properties and carbon fluxes following afforestation and agriculture in tropical forest

Anthropogenic land use change (LUC) affects soil quality and the global carbon (C) pool. Such LUC is a potential threat for forest ecosystems because it can alter soil biome and increases the emission of greenhouse gasses (GHGs). Here, we investigated the changes in soil quality and CO2 emission following afforestation of reclaimed coal mine land and agriculture land created in a tropical dry deciduous forest of Jharkhand, India. Soil samples were collected from afforested mined soil (AMS), agriculture soils (AGS), and the natural forest soils (NFS) and analyzed for physicochemical and biological properties. Soil infiltration rate and CO2 efflux were recorded in situ, and C balance and emission coefficient (Ci) were calculated to determine soil C dynamics.Our results demonstrated significant alteration in soil quality parameters (decreases/increases based on the individual parameter) in converted land use. Compared to NFS, soil organic carbon (SOC) stocks decreased by 84% in AMS and 50% in AGS, soil CO2 efflux increased by 35% in AGS and decreased by 43% in AMS, attributed to differences in vegetation and microbial activities among sites. Principal component analysis showed soil infiltration rate, total nitrogen, and clay content were highly influenced by the LUC and explicitly indicate soil quality. The 4-year old AMS was C negative and had a greater Ci value than AGS and NFS, probably due to the lesser vegetation cover and adverse soil properties. We concluded that the conversion of tropical forests to different lands altered soil quality that can be assessed using indicator parameters like soil infiltration rate, total nitrogen, and clay content. Such LUC tends to switch the forest from a sink to a source of CO2 whether the end use is afforestation or agriculture. However, land degradation due to surface mining activities had a greater impact on soil quality and C sequestration potential than agriculture.

Estimating the European CO2 emissions change due to COVID-19 restrictions

The carbon dioxide variations generated by the socio-economic restrictions imposed by the management of the COVID-19 crisis are analysed in this paper for 23 European countries and 10 economic sectors. By considering the most up to date information on GDP and carbon intensity of production, this paper represents one of the first attempts to estimate the CO2 emissions change that have taken place in Europe during the first six months of 2020. Results show that more than 195,600 thousand tons of CO2 have been avoided between January and June 2020, compared to the same period of the previous year, representing a −12.1% emissions change. The largest reductions have taken place in the Manufacturing, Wholesale, Retail Trade, Transport, Accommodation and Food Service sectors, accounting for more than 93.7% of total CO2 change. Spain, Italy and France have been the most affected areas with −106,600 thousand tons emissions drop. In line with the results provided by previous studies, this paper highlights that the geographical and the sectoral distribution of the CO2 emissions change has been largely influenced by the magnitude of the COVID-19 impacts. In addition, the carbon intensity of production, characterizing the most affected economic activities, has been the main element of differentiation compared to the previous 2008 crisis. By providing preliminary estimation of the CO2 emissions change that have taken place across geographical and sectoral activities, this paper contributes to the existing climate policy debate and can support future estimation of CO2 variations both in a context of confinement release as well as in a context of reintroduced COVID-19 restrictions.

Decomposing CO2 emission changes in thermal power sector: A modified production-theoretical approach

Carbon emission from the thermal power generation sector is one of the main contributors to global warming. It is critical to objectively evaluate the influence of relevant factors on carbon emissions, which may be beneficial for the implementation of policies and the formulation of development plans. However, the current decomposition approach based on production-theoretical decomposition analysis (PDA) and weak disposability may cause deviation on potential emission reduction. This paper proposes a modified PDA model under the assumption of semi-disposability and decomposes the carbon emission changes of China's thermal power generation sector accordingly. The results show that provinces, which have the highest non-disposal degrees during the study period, are mostly located along the east coast of China. The growth in installed capacity is identified as the largest contributor to the increase in national carbon emissions, while the decrease in capacity utilization rate significantly benefits the carbon emission reduction. A cluster analysis is also conducted to give policy implications for provinces with similar characteristics. Compared with traditional methods under strong and weak disposability assumption, the proposed model shows great advantages in identifying the potential emission reduction and reflecting the real production process, which makes it more suitable for decomposing the changes of carbon emissions and other environmental pollutants.

The effect of directed technical change on carbon dioxide emissions: evidence from China’s industrial sector at the provincial level

Technical change has a pivotal role to play in low-carbon development. Recent research has offered different insights regarding the effect of technical change on CO2 emissions but ignored the bias of technical changes which lead to changes in CO2 emissions. To fill the gap, this paper uses the 2008 to 2015 provincial-level data on China’s 22 industrial sub-sectors to investigate both the effect of directed technical change on CO2 emissions and its heterogeneity. We find that the technical change in most industrial sectors in China was capital-biased, although a labor-biased trend was evident. Labor-biased technical change is conducive to CO2 reduction, while capital-biased technical change has the opposite effect. Moreover, this effect is different by developmental periods, industries, and regions. Therefore, we propose that the government promotes labor-biased technical change based on the differentiated characteristics.

Will Coal Price Fluctuations Affect Renewable Energy Substitution and Carbon Emission? A Computable General Equilibrium-Based Study of China

Changes in the energy price system will determine the direction of evolution of the energy industry structure. As a country where coal is the dominant energy source, what is the effect of coal price fluctuations on China’s industry development costs and energy consumption structure? To investigate this problem, this paper utilized an economy–energy–environment computable general equilibrium model. In this study, four aspects were analyzed: Energy supply side, proportion of renewable energy consumption, macroeconomy, and changes in CO2 emissions. The results of this study show that an increase of 10%–20% in coal prices contributes to a shift into using renewable energy, which leads to energy saving and emission reduction. Renewable energy and clean energy rose by 0.57%–4.47% in the energy structure, but this has a certain negative impact on the macroeconomy. The gross domestic product (GDP) fell by 0.07%–0.18%. As a result, the decline in coal prices became an obstacle to renewable energy substitution and energy conservation. In addition, we put forward policy suggestions according to the results in energy, economic, and environmental effects.

The climate in climate economics

We develop a generic and transparent calibration strategy for climate models used in economics. The key idea is to choose the free model parameters to match the output of large-scale Earth System Models, which are run on pre-defined future emissions scenarios and collected in the Coupled Model Intercomparison Project, Phase 5 (CMIP5). We propose to jointly use four different test cases that are considered pivotal in the climate science literature. Two of these tests are highly idealized to allow for the separate quantitative examination of the carbon cycle and the temperature response. Another two tests are closer to the scenarios that arise from economic models. They test the climate module as a whole, that is, they incorporate gradual changes in CO2 emissions, exogenous forcing, and ultimately the temperature response. To illustrate the applicability of our method, we re-calibrate the free parameters of the climate part of the seminal DICE-2016 model for three different CMIP5 model responses: the multi-model mean as well as two other CMIP5 models that exhibit extreme but still permissible equilibrium climate sensitivities. As an additional novelty, our calibrations of DICE-2016 allow for an arbitrary time step in the model explicitly. By applying our comprehensive suite of tests, we show that i) both the temperature equations and the carbon cycle in DICE-2016 are miscalibrated and that ii) by re-calibrating their coefficients, we can match all CMIP5 targets we consider. Finally, we apply the economic model from DICE-2016 in combination with the newly calibrated climate model to compute the social cost of carbon and the optimal warming. We find that in our updated model, the social cost of carbon is similar to DICE-2016. However, the optimal long-run temperature in our calibration lies almost one degree below that obtained by DICE-2016. Moreover, the social cost of carbon turns out to be much less sensitive to the discount rate than in DICE-2016. We explain how the model's climate part relates to these differences and also show that under the optimal mitigation scenario, the temperature predictions of DICE-2016 (in contrast to our proposed calibration) fall outside of the CMIP5 scenarios, suggesting that one might want to be skeptical about policy predictions derived from DICE-2016.

Change in energy demand and CO<SUB align="right">2 emission in Turkey from the period 1981-1998 to the period 2007-2019

Due to population increase and change in climate, energy demand and CO2 emissions have risen. This study investigated the effects of climate and population change on energy demand in Turkey between periods of 1981-1998 and 2007-2019. In addition, CO2 emissions based on heating and cooling degree-days were examined since CO2 release with increased energy consumption is a major environmental impact that can contribute to climate change. The results showed that total heating degree-days decreased by 11.21% and total cooling degree-days increased by 38.98% due to climate change. The total product of population heating and cooling degree-days were increased by 14.42% from 1981-1998 to 2007-2009. CO2 emissions from heating in 78 cities decreased, while CO2 emissions from cooling in 77 cities increased. Results can be useful for planning future energy consumption in buildings as the climate changes.

Change in energy demand and CO<SUB align="right">2 emission in Turkey from the period 1981-1998 to the period 2007-2019

Due to population increase and change in climate, energy demand and CO2 emissions have risen. This study investigated the effects of climate and population change on energy demand in Turkey between periods of 1981-1998 and 2007-2019. In addition, CO2 emissions based on heating and cooling degree-days were examined since CO2 release with increased energy consumption is a major environmental impact that can contribute to climate change. The results showed that total heating degree-days decreased by 11.21% and total cooling degree-days increased by 38.98% due to climate change. The total product of population heating and cooling degree-days were increased by 14.42% from 1981-1998 to 2007-2009. CO2 emissions from heating in 78 cities decreased, while CO2 emissions from cooling in 77 cities increased. Results can be useful for planning future energy consumption in buildings as the climate changes.