Assessing carbon dioxide emission reduction potential using a cost approach

Transport is an essential infrastructure for development. With its high share of gross domestic product (GDP), it makes a significant contribution to total CO2 emissions in Malaysia. It is therefore important to pay greater attention to reducing CO2 emissions and sustainable development in this sector. Therefore, this study aims at estimating the relationship between transport CO2 emissions and its key drivers using the Autoregressive Distributed Lag (ARDL) technique. The time period covered by the study extends from 1978 to 2018. It further investigates the response of CO2 emissions to shocks in the value of other variables by employing the generalized impulse response approach. The results suggest that urbanization is the major contributor to the increase in CO2 emissions followed by the carbon intensity of energy in the long-run. Carbon intensity of energy, GDP per transport worker and urbanization contribute significantly to increases in transport CO2 emissions in the short- and long-run. Testing the Environmental Kuznets Curve (EKC) hypothesis recommends that Malaysia continue to be on track to reach the highest level of income and welfare to give pay more attention to the environment. Therefore, the country maintains its CO2 emissions level in the future because of economic development. Therefore, these findings show that energy and environmental policymakers need to pay more attention to improving energy efficiency and the use of low-carbon technologies and electrification in the transport sector and the use of high-quality public transport, particularly in urban areas, for sustainable urban development.

Technological pathways to decarbonisation and the role of renewable energy: A study of European countries using consumption-based metrics

CO2 contributes a lot to the greenhouse effect. The total CO2 emissions of the two countries, China and the USA, as the world's top two economies, have exceeded 40% of the total global carbon emissions. In this context, the exploration of the evolution of carbon emissions from energy consumption in China and the USA and the comparison of the characteristics of carbon emission drivers in different periods play a significant role in the policy formulation and climate change cooperation between China and the USA. In this study, the BP structural breakpoint test was used to divide the carbon emission stages of China and the USA from 1970 to 2019. The generalized Divisia index model (GDIM) was developed to decompose the growth of carbon emissions in China and the USA into eight items, GDP, carbon intensity of GDP, energy use, carbon intensity of energy, population, carbon emissions per capita, GDP per capita, and energy intensity, and to analyze the characteristics and cumulative contribution of carbon emission drivers at each stage. Based on the stage and cumulative characteristics of carbon emissions between China and the USA, the USA should take the initiative to assume the legal responsibility of carbon emissions and further deepen the cooperation with other countries in the field of climate change. China should transform the economic growth mode, optimize the energy structure, and improve the efficiency of resource utilization to help achieve the peaking carbon emissions and the carbon neutrality smoothly.

In order to be able to meet the commitments with regard to its Nationally Determined Planned Contribution submitted to COP 21, Tunisia is committed to effectively reducing its CO2 emissions. Our study detects the main potential factors responsible for the increase in carbon dioxide emissions in Tunisia using the decomposition method (Logarithmic Mean Divisia Index: LMDI). Then, an analysis of the long-term relationship between CO2 emissions and these main factors is established through an error correction model. The forecast of this cointegration relationship by 2030 shows that Tunisia has significant potential for reducing CO2 emissions, especially through the phenomenon of reduction in energy intensity. However, political intervention beyond energy control is necessary in order to achieve Tunisia's environmental objectives.

Investigating the linkage between financial development (FD) and carbon emissions is important for mitigating climate change. Nevertheless, there is a scarcity of studies investigating how carbon emissions decouple from FD. Here, we investigate the relationship between FD and carbon emissions by using the decoupling model based on cross-province data of China during 2000–2019. Then, we use the decomposition method to analyze the nine drivers of decoupling elasticity of FD and CO2 emissions. We found that China experienced weak decoupling and strong negative decoupling in most years. Only the finance develops at a very high level; the FD had spare capacities to promote the reduction in the carbon emissions. For example, several developed provinces (e.g., Tianjin, Zhejiang, Guangdong) realized strong decoupling after 2012. The reduction in energy intensity and the increase of foreign direct investment promoted the decoupling of FD from carbon emissions. During the financial recession period, developing a bank-based financial market helped the emissions reduction. Once financial crisis is overcome, developing a market-based financial market promoted the decoupling of FD from emissions. This is because that with the fast FD, the development of stock market contributed to emission reductions through technological improvement, while the bank loans inhibited the decoupling process through the expansion of capital-labor inputs. Overall, these results help in the assessment of the emissions impacts of FD and in addressing climate change problems.

Impact of energy efficiency on CO2 Emissions: Empirical evidence from developing countries

ABSTRACTThe world's energy systems have been critical in the face of the growing threat of climate change. The energy transition progress proved unsuccessful at COP27, while COP28 provided an opportunity to question whether energy systems need a revolution to avoid climate disasters and attain carbon neutrality goals. In this regard, the present study aims to explore whether structural changes in the energy sector can help nations achieve carbon neutrality. For this purpose, we empirically analyze the impact of both non‐renewable and renewable energy sources on CO2 emissions and carbon intensity of energy. Specifically, we targeted the top five climate‐laggard economies—that is, New Zealand, Australia, the Philippines, Mexico, and Indonesia—which are making efforts toward carbon neutrality but are lagging in the COP28 race due to their high reliance on fossil fuels in their total energy mix. We employed the Cross‐Sectionally Augmented Auto‐Regressive Distributed Lag (CS‐ARDL) model on data collected from 1990 to 2022 to examine the findings empirically. The findings of this research showed that non‐renewable energy sources are likely to lead to higher emissions of CO2 and higher carbon intensity. On the other hand, renewable energy negatively affects both CO2 emissions and carbon intensity. Thus, the results obtained in this study indicate that renewable energy adoption plays a crucial role for countries intending to achieve the goals set at COP28 and advance their efforts toward carbon neutrality.

In the Yangtze River Economic Belt (YREB), one of the most important challenges at present is to promote green, low-carbon development. This study attempted to explore the impact of different dimensions of urbanization and technology on CO2 emissions at different stages in YREB by using an extended STIRPAT model on provincial panel data from 2000 to 2017. To examine the change differences based on the different effects of urbanization and technology on CO2 emissions, we divided the total study period into two stages according to the change trends of CO2 emissions and considered the YREB as a whole as well as the lower, middle, and upper reaches individually. The main findings are as follows. First, an inverted U relationship was found between economic urbanization and CO2 emissions for the entire study period along with the period of a rapid rise in CO2 emissions (Stage I) only in YREB and the upper reaches, while in the stable change period (Stage II), the inverted U relationship existed in the upper and lower reaches. An inverted U relationship between technology and CO2 emissions was only found in the middle reaches for Stage I and in the middle and lower reaches for Stage II. Second, during the entire study period, economic urbanization had the greatest inhibitory effect on carbon dioxide emissions, followed by energy intensity and population urbanization; during Stage I, the main reduction factors were economic urbanization and energy intensity, and population urbanization had a non-significant impact. Third, per capita gross domestic product (GDP) and population size had a positive impact on CO2 emission increases. Specifically, during Stage II, the fitting effect was not good (R2 is 0.3948), and the whole formula was not significant. In lower reaches, the economic urbanization had a positive impact at Stage I, the energy intensity had a rebound effect and per capita GDP had a non-significant impact at Stage II.

The agglomeration of carbon emissions from the pharmaceutical and health industry has been growing gradually in Beijing. Conducting research on the path of co-control of pollution and carbon emission reduction in industrial parks is essential to realize synergy between economic development, pollution reduction, and green low-carbon. Based on the production activity data in 2020 of two typical parks of manufacturing and R&D, we selected six measures for co-control of pollution and carbon emission reduction and set up a synergistic development scenario to explore the collaborative development path of different types of parks. The results showed that： ① The primary carbon emission source in the two parks was energy consumption, such as natural gas and electricity, whereas the major pollution source was from key polluting enterprises. The emissions in the R&D park were significantly lower than those in manufacturing parks, with atmospheric pollutant emissions accounting for 25% of the manufacturing park's emissions. ② Energy structure and intensity, along with pollutant emission reduction measures, contributed 62.6% and 37.4% to pollution and carbon emission reduction for the R&D park and 81.6% and 13.5% for manufacturing park, respectively. ③ Adjusting the energy structure of the park and prioritizing the management of key polluting enterprises could achieve synergistic emission reduction, with the rate of emission reduction primarily reflecting atmospheric pollutants. Reducing energy intensity could also facilitate synergistic emission reduction, with a rapid rate of carbon emission reduction. Optimizing the industrial structure could lead to different degrees of pollution and carbon emissions increasing synergistically or not synergistically, owing to the particularity of the industrial structure of the park. ④ Applying measures to adjust the energy structure and reduce energy intensity and pollutant emissions into the collaborative path should be a priority. The governance scope of key polluting enterprises should be appropriately expanded in manufacturing parks. The synergistic effect of public environmental protection facilities of R&D parks should be focused on, in addition to reducing corporate pollutant emissions. Measures to optimize industrial structure at the park level should be adjusted to local conditions and scientifically guide industrial transformation and the settlement of high-tech enterprises.

Decoupling economic growth from carbon dioxide emissions in China: A sectoral factor decomposition analysis

PurposeThis paper aims to explain the empirical relationship between competitiveness and economic growth in a globalizing world. In recent times, the advanced economies have experienced a slowdown of growth, whereas the BRICS countries continue to experience high growth. The authors explore the following question: Does competitiveness of nations’ degree of competition explains this differential in growth? The authors explore competiveness and growth in a macroeconomic perspective for the large economies in the OECD and BRICS countries.Design/methodology/approachThe authors use dynamic panel data modelling technique to find the relationship between competitiveness and economic growth. This technique enables to control heterogeneity problem of this group to some extent. The focus variable of this study is annual GDP growth rate for the period 2007-2017. The proxies for measuring competitiveness in this paper are trade as percentage of GDP, product market regulation, unit labour cost and global competitive index. Innovation prevalence of foreign ownership, efficiency, competition, state of cluster development, venture capital availability, extent of market, research and development expenditure as percentage of the GDP mergers and acquisitions and multifactor productivity are the control variables.FindingsThe authors find that the degree of competitiveness competition is less likely to impact economic growth in the OECD countries because they have more or less similar competitive environment. Innovation, extent of market and state of cluster development and venture capital availability explaining growth differential. Increased competition is likely to affect growth negatively. This explains the oligopolistic structures of the world economy. However, the BRICS countries vary significantly in competitive environment. This is the reason of volatility in their growth. The conclusion is that competitiveness is important for sustained growth. Competitiveness is, however, an outcome of a set of policies, not a policy itself.Research limitations/implicationsProductivity data for OECD and BRICS countries are not available. Various series are not comparable. OECD countries have discontinued yearly unit labour cost series, and high frequency series are available but no such series for BRICS exists.Practical implicationsFirst, this paper proposes that wage growth, measured by the unit labour cost growth rate, is an important determinant of competitiveness amongst the nations. Wage growth is falling short of productivity growth in the OECD countries. This has implications for the long run sustainability of growth, skill development and inequalities in the region. Since 2011, world economic recovery is slow. Wage growth is imperative for generating sufficient private demand in the OECD countries. Second, this paper provides evidence that competitiveness is important for explaining growth in the OECD and the BRICS countries. However, it also highlights that competitiveness can be measured effectively by the trade differential or with the help of unit labour cost. Unaligned real effective exchange rate in terms of unit labour costs is the real cause of the problem.Originality/valueResearch in this area is still in infancy. This research finds that how competitiveness affects growth. A more competitive nation can sell more, but not necessarily grow rapidly. In development process, growth comes first, and at the latter stages, countries have to introduce effective reforms for competitiveness. This is the effect of competitiveness on growth by comparing various indexes.

CO2 emissions change in Tianjin: The driving factors and the role of CCS

Does financial sector development mitigate power sector-based carbon dioxide emissions to establish environmental sustainability in BRICS?

Impact of industrialisation on CO2 emissions in Nigeria

The household sector has become the second-largest source of energy consumption and CO2 emissions in China. It is important to understand the trends and changing mechanisms of household energy-related CO2 emissions (HECEs) in different social stages for mitigating the impact of climate change. However, the existing trends in HECEs and whether they are congruent with the expectation that HECEs in later developed areas are lower than those in priority areas under the same economic level are unclear. Here, we compared the per capita HECEs (PHECEs) of urban agglomerations under the same economic standards and analyzed the causes of the comparative results. We find that (1) HECEs increased rapidly from 3.65 × 108 t to 12.42 × 108 t during 1995-2017, with an average annual growth rate of 14.19%; (2) urban agglomerations that developed earlier do not have higher PHECEs. The PHECEs of urban agglomerations with moderate and later development do not decrease under increased social, technological, and cognitive conditions; (3) carbon intensity (CI), energy intensity (EI), and per capita regional gross domestic product (PCG) have a positive impact on PHECEs, and the potential impact of EI on PHECEs reduction is greater; (4) the effects of CI, EI, and PCG on PHECEs in different urban agglomerations are various. However, these effects are similar within urban agglomerations. Our study provides a reference for reducing CO2 emissions in the household sector and for the green development of urban agglomerations and emphasizes that the growth of HECE at a reasonable level is necessary when technology does not meet sufficient constraints.