Variation Of Carbon Emissions Research Articles

Exploring the Nexus between CO2 Emissions, Trade, and Sustainable Economic Growth: A Novel NARDL Approach

This study investigates the nexus between CO2 emission, trade, Gross domestic product (GDP), and tourism in Pakistan during 1990-2019. We examined the asymmetric relationship between CO2 emission, international trade, Gross domestic product (GDP), and international tourism number of arrivals in Pakistan. Data Stationarity has been tested using the Augmented Dicky Fuller (ADF), Kwiatkowski-Phillips-Schmidt-Shin (KPSS), and Phillips-Perron (PP) tests for unit roots. We used the NARDL technique. Then a Granger Causality helped estimate the relationship between these environmental variables. If there is an asymmetric association between these variables, we estimate the non-linear auto-regressive distributed lag, which is the most applicable econometric estimate. The ARDL technique can potentially explore the dynamic nexus between CO2 emission, tourism, GDP, and trade. Our findings suggest a dynamic relationship between tourism, trade, CO2 emission, and GDP from Pakistan's perspective. The outcomes reveal that negative and positive shocks to trade, GDP, and tourism affect CO2 emissions in the short and long term. The Granger Causality findings also determined that causality is directed from Tourism toward CO2 emission, while unidirectional causality was found between CO2 emission and tourism. In this study, the graphs of the CUSUM and CUSUMQ determined the functional stability of the established relationship. This indicates that this model is a suitable and valuable strategy tool. Therefore, an increase or decrease in the macroeconomic series will cause variation in carbon emissions in Pakistan in the long -term. The finding benefits macro-level policymakers and will provide important insight for the relevant stakeholders.

Spatiotemporal Variation in Carbon Emissions in China’s Tourism Industry during the COVID-19 Pandemic and Ecological Compensation Mechanism

Spatiotemporal Variation in Carbon Emissions in China’s Tourism Industry during the COVID-19 Pandemic and Ecological Compensation Mechanism

Spatiotemporal evolution and influencing factors of land-use emissions in the Guangdong-Hong Kong-Macao Greater Bay Area using integrated nighttime light datasets

Carbon emissions from land-use and land-cover change (together referred to as ‘land-use emissions’) are an important way to influence the regional carbon balance. However, due to the limitations and complexity of obtaining carbon emissions data at spatial scales, previous studies rarely reveal the long-term evolution characteristics of regional land-use emissions. Therefore, we propose a method to integrate DMSP/OLS and NPP/VIIRS nighttime light images to calculate land-use emissions over a long time series. The accuracy validation results show that the integrated nighttime light images and land-use emissions have a good fit and can accurately assess the long-term evolution of regional carbon emissions. In addition, by combining the Exploratory Spatial Analysis (ESTDA) model and the Vector Autoregressive Regression (VAR) model, we found significant spatial variation in carbon emissions in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), with the two regional emission centres spreading outwards between 1995 and 2020, with an increase in construction land area of 3445 km2, resulting in 257 million tons (Mt) of carbon emissions over the same period. The rapid increase in emissions from carbon sources is not offset by a correspondingly large amount of carbon sinks, resulting in a serious imbalance. Controlling the intensity of land use, optimizing the structure of land use and promoting the transformation of the industrial structure are now the keys to achieving carbon reduction in the GBA. Our study demonstrates the enormous potential of long-time-series nighttime light data in regional carbon emission research.

Micro-macro multilevel analysis of day-to-day lifestyle and carbon emissions in UK multiple occupancy households

Far-reaching changes in daily life present a pressing need to balance energy consumption with environmental impact. Previous research on household carbon emissions generally described its contributors in disposable income, consumption pattern, and household-related lifestyle, whereas they have not fully explored how carbon emissions relate to residents' day-to-day lifestyles. Given that individual lifestyles within a household may be correlated, there is a need to disentangle the clustering effect of household members' lifestyles and their association with household carbon emissions. This study used micro-macro multilevel modelling to examine the structure of individual lifestyles and their impact on household carbon emissions for 8618 multiple occupancy households of 19,816 respondents in the UK Household Longitudinal Study dataset. The results showed that a factor capturing energy-saving lifestyle behaviours significantly reduced housing fuel use emissions and a second capturing transportation and consumption choices cut motor emissions. Interestingly, the contribution of energy-saving lifestyle in cutting down housing-fuel-using emissions becomes more pronounced when household income and household characteristics (e.g., household size, dwelling, house ownership, number of cars, urbanity, employment) were controlled for. Contrarily, the strength of green transportation and consumption lifestyle contributing to lower motor emissions was weakened after controlling for household characteristics. Findings indicated that day-to-day lifestyle not only reflects individual variability in sustainable living but also systematic household variation in carbon emissions. Knowledge of which living patterns are responsible for disproportionately high levels of carbon emissions can enhance effective targeted policy aimed at stimulating sustainable lifestyles and carbon reduction.

Modeling carbon emission estimation for hinterland-based container intermodal network

In order to deal with the increasing environmental challenges, the high carbon-emitting transport system keeps transitioning to achieve carbon peaking and carbon neutrality goals. This paper addresses the calculation of carbon emission estimation and identifying carbon reduction driving factors for the hinterland-based container intermodal network. Firstly, an analysis of the carbon emissions composition of the hinterland-based container intermodal networks is conducted. Furthermore, a carbon emission estimation model for the hinterland-based container intermodal network is developed. Subsequently, taking the Shanghai port and the Yangtze River Delta (YRD) hinterland as a case, we estimate the carbon emission of the hinterland-based container intermodal network under the Well-to-Wheel (WTW) and the Tank-to-Wheel (TTW) modes in the past ten years. Finally, the critical driving factors are identified by sensitivity analysis, and the variation of carbon emissions under different scenarios with carbon reduction policies are analyzed. The results indicate that the transportation structure and the fuel choices affect network emissions reductions significantly. In the current policy-based scenario, the WTW mode achieves the carbon peaking between 2030 and 2035, while the TTW mode peaks earlier.

Does green innovation, energy productivity and environmental taxes limit carbon emissions in developed economies: Implications for sustainable development

The present study considers environment related tax (ERT), energy productivity (ENEP), green innovation (GI), renewable energy (REC), and gross domestic product (GDP) that explains the variation in carbon emissions of the group of seven (G-7) from 1990 to 2020. The empirical analyses include various diagnostic tests, followed by advanced econometric techniques of panel cointegration, Method of Moment Quantile Regression (MMQR), and Dumitrescu & Hurlin panel causality. The findings reveal the presence of cross-sectional dependence, heterogeneous slope models, and mixed order of integration. Moreover, long term cointegration association is confirmed by Westerlund panel cointegration. The results of MMQR report that renewable energy, green innovation, and environmental taxes significantly mitigate carbon emissions, while GDP cause rise in CO2 emissions in the G-7 economies. These results are also validated by the outcomes of Quantile Regressions. Finally, a bi-directional causal association is confirmed amongst all the factors under investigations in this study. The G-7 economies should focus on renewable energy consumption (REC), encouragement and support of green innovations (GI) and initiatives to boost energy productivity (ENEP). Moreover, the policy makers should consider the crucial coordination among environmental taxes, energy productivity, renewable energy, green innovation as these factors have a strong influence on each other.

Impact of Urbanization and Industrial Structure on Carbon Emissions: Evidence from Huaihe River Eco-Economic Zone

Rapid urbanization in China greatly contributes to carbon emissions, while the industrial structure greatly contributes to changes in the variation of carbon emissions. This research addresses the impact of urbanization and industrial structure on carbon emissions from 2010 to 2018, by focusing on the Huaihe River Eco-economic Zone, which is an important economic corridor along the north–south division of China. Although many studies have focused on investigating the impact of urbanization or industrial structure on carbon emissions, few studies further addressed an analysis of the impact of both on carbon emissions, using multiple measurement models. This paper reveals the holistic and local impact of industrial structure and urbanization on carbon emissions, by integrating a threshold regression model with geographically weighted regression. The results are as follows: (1) From a holistic point of view, industrial structure and urbanization had both, single threshold, and double threshold effects on carbon emissions in the Huaihe River Eco-economic Zone. (2) From a regional perspective, the coefficients of industrial structure on carbon emissions were all positive, but the rate of increase gradually slowed down. The coefficients of urbanization on carbon emissions were all negative, reaching a maximum value of negative effect in 2013. Understanding the holistic and local impact of urbanization and industrial structure on carbon emissions provides governments with differentiated and forward-looking suggestions for mitigating carbon emissions in the Huaihe River Eco-economic Zone.

A study of carbon emissions and energy consumption of wind power generation in the Panhandle of Texas

It has been established that the carbon emissions of wind power depends on several factors, most importantly, the size, manufacturing location, operating location, and life expectancy of the turbine. However, there is significant variation in carbon emissions of wind power generation reported in the literature. This makes it necessary to study the emissions of a windmill of a given size operating in a given location. In this paper, we study the carbon intensity and energy intensity of wind generation from a Nordex 1.3 megawatts (MW) wind turbine located in the Panhandle of Texas. Our model has three components: (a) lifetime power generation model which uses Typical Meteorological Year 3 data from National Renewable Energy Laboratory, (b) process analysis model for raw materials used in manufacturing of the wind turbine, and (c) environmentally extended input–output analysis model for all other steps involved in the life cycle analysis of the windmill, including manufacturing, transportation, installation, operation, and maintenance of windmills. Our results show that a 1.3 MW windmill operating for 20 years in the Panhandle of Texas, generates 467 terra-Joules (TJ) of electricity while consuming 25.58 TJ of energy and producing 1870.52 million-grams (Mg) of CO2. The resulting carbon emission intensity of this turbine is estimated to be 14.45 gCO2/kWh. In 2019, the U.S. generated about 286.6 billion kWh of its electricity from the wind which amounts to 4.12 million ton (MT) of annual emissions and will increase substantially as deeper levels of wind generation is achieved in the next several decades.

Global Development, Converging Divergence and Development Studies: A Rejoinder

Global Development, Converging Divergence and Development Studies: A Rejoinder

Approach to establish carbon emission benchmarking for construction materials

The construction industry will play a crucial rule in global carbon emission reduction as the embodied carbon of construction materials shares an increasing portion of a building’s life-cycle carbon dioxide (CO2) emissions. In order to reduce CO2 emissions by selecting low-carbon construction materials, it is inevitable that a proper benchmarking mechanism and severity representation of different emission levels should be established, as benchmarking is a useful tool for pursuing consumer purchasing habits toward selection of low-carbon materials. In addition, high variation of carbon emissions is observed among the large number of studies and databases, and there is no consensus on how these should be used in practice. Therefore, standardization and benchmarking of emissions for construction materials are essential. In this study, a novel benchmarking approach was developed for embodied carbon emissions of commonly used construction materials by applying a fuzzy set theory. Given that a reliable local database is lacking, the benchmark was developed by referring to the existing carbon inventories, linguistic variables of severity levels from a questionnaire survey, and the fuzzy set analysis. A six-step method was proposed based on a horizontal and graphical approach. This study offers an alternative approach in benchmarking where a database or a set of real data is not available. It is expected that the developed benchmarks will help practitioners and decision-makers compare the embodied CO2 emissions of a specific batch of construction materials and select low-carbon materials in construction projects.

Analysis on the influencing factors of carbon emissions from energy consumption in China based on LMDI method

Based on the time series decomposition of the Log-Mean Divisia Index, this paper analyzes the driving factors of carbon emissions from energy consumption by introducing the indicators of energy trade in China during the period of 2000–2014. The carbon emissions are decomposed into carbon emission coefficient, population, economic output, energy intensity, energy trade, energy structure and industrial structure effect in the manuscript. The result indicates that economic activity has the largest positive effect on the variation of carbon emissions. The energy trade has a greatest opposite effect on carbon emission change. At the same time, China has achieved a considerable decrease in carbon emission mainly due to the improvement of energy intensity and the optimization of energy and industrial structure. However, the influences of those changes in energy intensity, energy and industrial structure are relatively small. In addition, through the analysis by using a suitable index of energy trade, it was found that improving the conditions of energy trade can effectively optimize the energy structure and reduce the carbon emission in China.

Exploration of life cycle data calculation: Lessons from a Passivhaus case study

Executing a life cycle carbon analysis on a building has two components to consider: the Embodied Carbon (EC) and In-use Carbon, the sum of these forms ‘the building carbon budget'. The in-use can be obtained through regulatory tools such as the Standard Assessment Procedure (SAP) in the UK. Embodied carbon has a loose framework with little guidance on a standardised methodology. This paper explores embodied carbon analysis using building components to enhance the understanding of the sensitivity and categorisation of measurements to propose a methodology. The exploration of differing methods on a Passivhaus case study was undertaken with the use of global warming potential identified as the correct unit of measurement. Different methods of estimating quantities and datasets used for an EC calculation are discussed. Results highlight a variation in carbon emissions for certain common building materials between the method used in the Environmental Performance Declarations (EPD) compared to current databases such as Inventory of Carbon and Energy (ICE) using Cradle to Gate data. Designers prefer simple embodied carbon calculation methods. This paper identifies a calculation method giving an acceptable accuracy with the least amount of input data required to implement regulatory standardisation within the industry.

Comparison of integrated assessment models: Carbon price impacts on U.S. energy

Integrated assessment models (IAMs) are increasingly used to evaluate carbon policy impacts on energy structure, but different models can yield considerably different results. This paper seeks to frame model results for policymakers and other consumers of model outputs. In this analysis we compare three models: GCAM, MERGE, and EPPA. We apply diagnostic carbon price scenarios, such as ramps and shocks, to identify key differences in model behavior that drive inter-model variability in projected policy impacts on the U.S. energy system. We report model results using several economic parameterizations and find that variation in carbon emissions across models results primarily from differences in carbon intensity of energy supply. These differences arise because models include different low-carbon energy technology options and vary widely in how flexible the electricity supply sector is at adapting to a change in policy. The timing of emissions abatement is also strongly influenced by whether the model is a simulation or an inter-temporal optimization scheme and the amount of foresight exhibited in the model. Our analysis demonstrates the usefulness of novel IAM diagnostic indicators and clarifies model features that are highly relevant for consumers of model results.

Regional Variation in Carbon Emissions and its Driving Forces in China: An Index Decomposition Analysis

This paper aims to investigate the factors affecting carbon emissions from the perspective of residential energy consumption. Specifically, through an index decomposition analysis (IDA), it examines the influence of social and economic factors on household carbon emissions in China's provinces. The results show that CO2 emissions associated with household consumption continued to grow during the decade of 2000–2010. In general, per capita consumption expenditure and the share of energy consumption expenditure in total consumption spending are two main factors contributing to an increase in residential carbon emissions, while the change of energy consumption structure and energy price mitigated the growth of carbon emissions. However, there are considerable regional differences. Therefore, Chinese government must consider the substantial contribution of residential consumption to carbon emissions when it aims to expand domestic consumption.