Industrial Carbon Dioxide Emissions Research Articles

Scenarios for deep decarbonisation of industry in Lithuania

Achieving an ambitious EU long-term vision of climate neutrality requires efforts across all Member States and economic sectors. Lithuania's industrial carbon dioxide (CO2) emissions are expected to maintain current trends under a business-as-usual case. We applied a bottom-up modelling approach to analyse deep decarbonisation scenarios for the Lithuanian industry up to 2050 to advance the wider debate and provide insights into the measures needed to make a difference. The results show that the deep decarbonisation of the Lithuanian industry by 2050 is feasible. However, to avoid large-scale emissions from industrial processes, it is requisite to utilise green hydrogen (H2) as a feedstock for ammonia production and carbon capture and storage (CCS) for clinker production. It requires significant investments and increases operating costs. Without CCS and H2, only up to 17 % of industrial emissions could be avoided by 2050.

Atmospheric Carbon Sequestration Using Microalgae

This article outlines biotechnological methods that can help reduce atmospheric and industrial carbon dioxide emissions through the use of microalgae. A general description of microalgae was provided, and the most promising species for microalgal biotechnology were identified. The metabolic process by which microalgae capture and degrade carbon dioxide was described. The microalgae-based biotechnological systems and devices available today were analyzed. The key factors that need to be considered for the effective and successful use of microalgae were highlighted. Different products obtained from microalgal biomass after atmospheric carbon dioxide sequestration were overviewed.

An Asymmetric Analysis of Renewable Energy in Mitigating Carbon Emissions in Pakistan

To achieve sustainable growth, energy is incumbent because all modern production is based on it. Unfortunately, about 80% of the global population uses polluted energy, adversely damaging the environment. The present study asses the asymmetric role of renewable energy in the mitigation of CO2 emissions in Pakistan. The Non-linear ARDL econometric approach is used to estimate the empirical results of the time series data from 1980 to 2020. The study found that positive change in renewable energy adversely declines CO2 emissions while negative change increases CO2 emissions. Additionally, the HDI and trade openness lessens CO2 emissions, while urbanization increases CO2 emissions. The study recommends that Pakistan should promote eco-friendly consumption patterns. Pakistan should focus on promoting renewable energy resources, which will help combat the upsurge in industrial and housing carbon dioxide emissions.

Strategies to Control Industrial Emissions: An Analytical Network Process Approach in East Java, Indonesia

This study identified the main agents, problems, solutions, and strategies for lowering industrial carbon dioxide (CO2) emissions in the cement industry in East Java, Indonesia, by applying an analytical network process. Respondents included government officials, industrial representatives, and environmental experts. This study revealed that (1) regulators are the critical agents controlling emissions; (2) the three major problems faced when aiming to reduce industrial emissions are limited environmental knowledge, inadequate infrastructure, and unsound regulations; (3) the main solutions are education, socialization, and infrastructure improvement; and (4) the institutional approach is preferable to command-and-control and economic incentives. This suggests that policymakers should collaborate closely with regulators, firms, and communities to more effectively control emissions and encourage environmentally friendly industrial practices. Economic incentives are not preferable strategies, most likely because of insufficient environmental knowledge, market distortion due to subsidies, and low viability. However, the institutional approach incurs higher costs due to political, administrative, and legal processes. Parties may agree on achieving socioeconomic demands but not environmental output. The institutional approach also requires extra investment in education and socialization as well as government support for infrastructure development and a better regulatory framework.

Impact of global value chain and technological innovation on China's industrial greenhouse gas emissions and trend prediction.

The global value chain has introduced profound changes in international trade, economic development, and technology progress as well as greenhouse gas emissions worldwide. This paper investigated the impact of the global value chain and technological innovation on greenhouse gas emissions by introducing a partially linear functional-coefficient model based on panel data of 15 industrial sectors in China from 2000 to 2020. Moreover, the greenhouse gas emission trends of China's industrial sectors from 2024 to 2035 were predicted using the autoregressive integrated moving average model. The results showed that (1) Greenhouse gas emissions were affected negatively by global value chain position and independent innovation. Nevertheless, foreign innovation had the opposite effect. (2) The results of the partially linear functional-coefficient model implied that the inhibitory effect of independent innovation on GHG emissions decreased with an improvement in the global value chain position. (3) The positive effect of foreign innovation on greenhouse gas emissions increased and then, decreased as the global value chain position improved. (4) The prediction results indicated that greenhouse gas emissions will continue on an upward trend from 2024 to 2035, while industrial carbon dioxide emissions should peak at 10.21 Gt in 2028. This carbon-peaking goal would be achieved in China's industrial sector by actively improving the global value chain position. Addressing these issues will enable China to take full advantage of the development opportunities of participating in the global value chain.

Effect of green bonds, oil prices, and COVID-19 on industrial CO2 emissions in the USA: Evidence from novel wavelet local multiple correlation approach

This study explores the effect of green bonds, oil prices, and the coronavirus disease 2019 (COVID-19) pandemic on industrial carbon dioxide (CO2) emissions. In this context, this study examines the United States of America (USA), which is the biggest economy in the world, uses weekly data between March 6, 2020 and September 30, 2022, and applies a novel wavelet local multiple correlation (WLMC) approach under time-varying and frequency-varying perspective. The novel empirical findings shows that (i) there is a strong negative (positive) co-movement between industrial CO2 emissions and green bonds in the short-run (long-run); (ii) there is a strong positive (negative) co-movement between industrial CO2 emissions and oil price in the medium-run (long-run); (iii) there is a strong negative (positive) co-movement between industrial CO2 emissions and the COVID-19 pandemic in the medium-run (long-run); (iv) the oil price is the dominant factor, whereas there are changing effect of the variables on each other at different times and frequencies; and (vi) overall, there are long-run asymmetric and dynamic correlations between industrial CO2 emissions and variables. Hence, the empirical results highlight the asymmetric, time-varying, and frequency-varying effects of green bonds, oil prices, and the COVID-19 pandemic on industrial CO2 emissions by presenting fresh and novel evidence. Moreover, the study proposes policy implications for the USA government.

When will China's industrial carbon emissions peak? Evidence from machine learning.

The manufacture of products in the industrial sector is the principal source of carbon emissions. To slow the progression of global warming and advance low-carbon economic development, it is essential to develop methods for accurately predicting carbon emissions from industrial sources and imposing reasonable controls on those emissions. We select a support vector machine to predict industrial carbon emissions from 2021 to 2040 by comparing the predictive power of the BP (backpropagation) neural network and the support vector machine. To reduce noise in the input variables for BP neural network and support vector machine models, we use a random forest technique to filter the factors affecting industrial carbon emissions. The statistical results suggest that BP's neural network is insufficiently adaptable to small sample sizes, has a relatively high error rate, and produces inconsistent predictions of industrial carbon emissions. The support vector machine produces excellent fitting results for tiny sample data, with projected values of industrial carbon dioxide emissions that are astonishingly close to the actual values. In 2030, carbon emissions from the industrial sector will have reached their maximum level.

Carbon Dioxide Emission Evaluations in the Chinese Furniture Manufacturing Industry Using the IPCC Tier-2 Methodology

Abstract Estimating industrial carbon dioxide emissions at the national scale is crucial for China's carbon peak and carbon neutralization targets, as well as the low-carbon development of the Chinese furniture manufacturing industry. For this purpose, in this study the Intergovernmental Panel on Climate Change Tier-2 methodology was used to evaluate the carbon dioxide emissions of the Chinese furniture manufacturing industry at the national scale. The results show that carbon dioxide emissions increased from 219.50 × 10,000 tons of CO2 equivalent in 2000 to 850.68 × 10,000 tons of CO2 equivalent in 2019. Moreover, carbon dioxide emission intensity decreased from 9.50 tons of CO2 per million dollars to 1.73 tons of CO2 per million dollars in this period. Moreover, electricity and raw coal were observed to have a significant influence on carbon dioxide emissions, followed by diesel oil, gasoline, heat energy, and natural gas. The results reveal that the Chinese furniture manufacturing industry has generally realized low-carbon development over the past two decades. This work proposes several suggestions to reduce carbon dioxide emissions from the Chinese furniture manufacturing industry, including promoting the use of clean electricity, the installation of photovoltaic cells, industrial transformation and upgrading, the optimization of transport modes for product delivery and material supply, and the employment of low-carbon raw materials.

Exploring the Impact of Industrial Land Price Distortion on Carbon Emission Intensity: Evidence from China

In the context of tax sharing reform and land reform during the 1990s, local governments in China relied heavily on land finance. Local governments have fierce competition in attracting investment, omitting the development of green economy. Based on the data of industrial land sales and carbon dioxide emissions, this study constructed the panel data of 196 cities in China from 2007 to 2017 and analyzed the spatial and temporal evolution characteristics of urban industrial land price distortion and carbon emission intensity. Furthermore, a multiple linear regression model was constructed from the aspects of scale effect to empirically analyze the overall impact and differential impact of urban industrial land price distortion on carbon emission intensity. With the help of the mediating effect model, the mechanism of urban industrial land price distortion on carbon emission intensity was investigated from the perspective of industrial structure upgrading. The results showed that (1) the higher the price distortion of industrial land is, the more detrimental it is to the development of green and low-carbon economy. (2) The regional heterogeneity test showed that the impact of industrial land price distortion on carbon emission intensity is most significant in the central region, medium cities, and cities with low fiscal self-sufficiency rates, respectively. (3) The higher the distortion degree of industrial land price, the greater the restriction on the upgrading of industrial structure, further increasing the carbon emission intensity. This paper provides policy implications for the market-oriented reform of land factors and the realization of the “double carbon” goal.

Intelligent Manufacturing and Carbon Emissions Reduction: Evidence from the Use of Industrial Robots in China.

Driven by the information technology revolution, using artificial intelligence to promote intelligent manufacturing while achieving carbon emissions reduction is increasingly the focus of international attention. Given this, based on the fact that China's industrial manufacturing is more intelligent, this paper uses industrial sector data and robot data from 2000 to 2017 to examine the impact of intelligent manufacturing on industrial carbon dioxide emissions and to discuss its internal mechanism. The research found that intelligent manufacturing significantly inhibits carbon dioxide emissions in the industrial sectors. The emission reduction effect is more obvious in industries with higher carbon emissions and intelligence. The mechanism test shows that intelligent manufacturing mainly achieves industrial emission reduction by reducing fossil energy consumption in the production process and improving energy use efficiency. The research findings of this paper provide favorable evidence for using new technologies, such as artificial intelligence, to achieve carbon emissions reduction, and validate the importance of intelligent manufacturing in tackling climate change in the future. It provides an essential reference for developing countries to use artificial intelligence for their carbon emissions reduction goals.

Grey Correlation Analysis of Low-Carbon Governance in Yangtze River Delta Cities.

The excessive emission of carbon dioxide will bring unpredictable ecological crisis, so it is particularly urgent to study the related factors of carbon emissions. Based on the grey correlation model, 31 factors in 5 aspects are selected as the reference frame for low-carbon governance, and the grey correlation degree of urban carbon emissions is calculated by using the IPCC method to calculate the annual carbon emissions of 9 major cities in the Yangtze River Delta from 2010 to 2019. Through the calculation and analysis of panel data, the following conclusions are drawn: The allocation of urban environmental practitioners is an important factor in carbon governance, and the reasonable and scientific allocation of environmental practitioners can have a significant impact on low-carbon development; the relationship between the amount of industrial power consumption and carbon dioxide emissions is not significant. On the contrary, the power consumption of urban residents can well reflect the level of carbon emissions. High residential power consumption means that the living standard of the people in the region is high, and the corresponding resource and energy consumption is large, so the carbon emissions will increase; the size of population density is particularly important for carbon governance, which is more obvious in economically developed areas. Urban economic development will inevitably lead to the improvement of people's quality of life, a stronger demand for resources, and a significant increase in carbon emissions.

Does drought increase carbon emissions? Evidence from Southwestern China

The study estimates the impact of the 2009/2010 drought in southwestern China on industrial outcomes and carbon dioxide (CO2) emissions. We focus on the outputs of the power and energy-intensive sectors and investigate the substitution of thermal power for hydropower during this extreme drought. Panel data for 93,830 firms from 2006 to 2013 were used to examine their responses to this extreme climatic event. We find that severe drought reduced hydropower production as well as the economic output of energy-intensive sectors, while it increased the power production of thermal power firms. As a result, the net CO2 emissions in the southwest increased by 6,704,364 tons, about 0.64% increase of the total regional CO2 emissions each year from 2009 to 2013. These findings suggest that climate disasters may increase carbon emissions, thereby contributing to climate change.

Peaking Industrial CO2 Emission in a Typical Heavy Industrial Region: From Multi-Industry and Multi-Energy Type Perspectives.

Peaking industrial carbon dioxide (CO2) emissions is critical for China to achieve its CO2 peaking target by 2030 since industrial sector is a major contributor to CO2 emissions. Heavy industrial regions consume plenty of fossil fuels and emit a large amount of CO2 emissions, which also have huge CO2 emissions reduction potential. It is significant to accurately forecast CO2 emission peak of industrial sector in heavy industrial regions from multi-industry and multi-energy type perspectives. This study incorporates 41 industries and 16 types of energy into the Long-Range Energy Alternatives Planning System (LEAP) model to predict the CO2 emission peak of the industrial sector in Jilin Province, a typical heavy industrial region. Four scenarios including business-as-usual scenario (BAU), energy-saving scenario (ESS), energy-saving and low-carbon scenario (ELS) and low-carbon scenario (LCS) are set for simulating the future CO2 emission trends during 2018–2050. The method of variable control is utilized to explore the degree and the direction of influencing factors of CO2 emission in four scenarios. The results indicate that the peak value of CO2 emission in the four scenarios are 165.65 million tons (Mt), 156.80 Mt, 128.16 Mt, and 114.17 Mt in 2040, 2040, 2030 and 2020, respectively. Taking ELS as an example, the larger energy-intensive industries such as ferrous metal smelting will peak CO2 emission in 2025, and low energy industries such as automobile manufacturing will continue to develop rapidly. The influence degree of the four factors is as follows: industrial added value (1.27) > industrial structure (1.19) > energy intensity of each industry (1.12) > energy consumption types of each industry (1.02). Among the four factors, industrial value added is a positive factor for CO2 emission, and the rest are inhibitory ones. The study provides a reference for developing industrial CO2 emission reduction policies from multi-industry and multi-energy type perspectives in heavy industrial regions of developing countries.

The U.S. Bioeconomy: Charting a Course for a Resilient and Competitive Future

The U.S. Bioeconomy: Charting a Course for a Resilient and Competitive Future

Determinants and regional contributions of industrial CO2 emissions inequality: A consumption-based perspective

The assessment of industrial consumption-based carbon dioxide emissions could explain interregional differences in carbon dioxide emissions resulting from economic activity. This study aims to assess per capita industrial consumption-based carbon dioxide emissions inequality across provinces, municipalities, and autonomous regions in China from 2002 to 2017. The status and main determinants of per capita industrial consumption-based carbon dioxide emissions inequality are calculated using environmentally multi-regional input–output models and the Theil index, respectively. Comparisons with accounting results from the production side are then analyzed. The main conclusions are as follows: (1) From 2002 to 2012, both per capita industrial consumption-based carbon dioxide emissions and per capita industrial production-based carbon dioxide emissions in China show an obvious upward trend, and gradually stabilize in 2015 and beyond. (2) Economic trade leads to obvious carbon leakage among Chinese provinces and industrial sectors: the consumption-based carbon dioxide emissions of the downstream sector of the industrial supply chain is less than its production-based carbon dioxide emissions, while the carbon dioxide emissions of upstream industrial sectors is on the contrary. (3) China's per capita industrial production-based carbon dioxide emissions inequality is significantly greater than consumption-based inequality, and this gap is gradually widening. Among them, between-group inequality is the key component, but the contribution of within-group inequality is gradually increasing, which tends to exceed between-group inequality. (4) Among the eight economic groups in China, per capita consumption is the main driving factor of consumption-based carbon dioxide emissions inequality, but the industrial production carbon dioxide emissions intensity is the main contributor to production-based inequality. The above findings would provide certain theoretical support and policy references for policymakers when formulating industrial carbon emissions reduction policies.

The UK industrial decarbonisation strategy revisited

Since 2010, successive UK governments have produced various strategies for industry to reduce carbon dioxide emissions (‘decarbonise’). This paper scrutinises the most recent version that was published in March 2021: the Industrial Decarbonisation Strategy (IDS). It contrasts the policy content of the IDS with previous industrial roadmaps, action plans and strategies (including the Clean Growth Strategy of 2017). In addition, it compares the proposals in the IDS with the latest recommendations of the UK government's independent Climate Change Committee, as well as drawing on lessons learned from the techno-economic assessments published by the author and his collaborators for a number of key ‘foundation industries’. The latter emit significant shares of UK industrial carbon dioxide (CO2) emissions: the iron and steel (∼25%), chemicals (∼19%), cement (∼8%), pulp and paper (∼6%) and glass (∼3%) sectors. They also produce about 28 Mt of materials per year, which are worth £52 billion to the UK economy, and account for ∼10% of UK total carbon dioxide emissions.

‘Spongy’ liquid could help to clean up industrial carbon dioxide emissions

‘Spongy’ liquid could help to clean up industrial carbon dioxide emissions

Spatial Heterogeneity of Factors Influencing CO2 Emissions in China’s High-Energy-Intensive Industries

In recent years, China has overtaken the United States as the world’s largest carbon dioxide (CO2) emitter. CO2 emissions from high-energy-intensive industries account for more than three-quarters of the total industrial carbon dioxide emissions. Therefore, it is important to enhance our understanding of the main factors affecting carbon dioxide emissions in high-energy-intensive industries. In this paper, we firstly explore the main factors affecting CO2 emissions in high-energy-intensive industries, including industrial structure, per capita gross domestic product (GDP), population, technological progress and foreign direct investment. To achieve this, we rely on exploratory regression combined with the threshold criteria. Secondly, a geographically weighted regression model is employed to explore local-spatial heterogeneity, capturing the spatial variations of the regression parameters across the Chinese provinces. The results show that the growth of per capita GDP and population increases CO2 emissions; by contrast, the growth of the services sector’s share in China’s gross domestic product could cause a decrease in CO2 emissions. Effects of technological progress on CO2 emissions in high-energy-intensive industries are negative in 2007 and 2013, whereas the coefficient is positive in 2018. Throughout the study period, regression coefficients of foreign direct investment are positive. This paper provides valuable insights into the relationship between driving factors and CO2 emissions, and also gives provides empirical support for local governments to mitigate CO2 emissions.

Application of Liquid Hydrogen Carriers in Hydrogen Steelmaking

Steelmaking is responsible for approximately one third of total industrial carbon dioxide (CO2) emissions. Hydrogen (H2) direct reduction (H-DR) may be a feasible route towards the decarbonization of primary steelmaking if H2 is produced via electrolysis using fossil-free electricity. However, electrolysis is an electricity-intensive process. Therefore, it is preferable that H2 is predominantly produced during times of low electricity prices, which is enabled by the storage of H2. This work compares the integration of H2 storage in four liquid carriers, methanol (MeOH), formic acid (FA), ammonia (NH3) and perhydro-dibenzyltoluene (H18-DBT), in H-DR processes. In contrast to conventional H2 storage methods, these carriers allow for H2 storage in liquid form at moderate overpressures, reducing the storage capacity cost. The main downside to liquid H2 carriers is that thermochemical processes are necessary for both the storage and release processes, often with significant investment and operational costs. The carriers are compared using thermodynamic and economic data to estimate operational and capital costs in the H-DR context considering process integration options. It is concluded that the use of MeOH is promising compared to the other considered carriers. For large storage volumes, MeOH-based H2 storage may also be an attractive option to the underground storage of compressed H2. The other considered liquid H2 carriers suffer from large thermodynamic barriers for hydrogenation (FA) or dehydrogenation (NH3, H18-DBT) and higher investment costs. However, for the use of MeOH in an H-DR process to be practically feasible, questions regarding process flexibility and the optimal sourcing of CO2 and heat must be answered.

The influence of poplar forest belts on agrocenoses in the forest-steppe zone of the Altai Territory

The article considers the influence of protective forest strips forest strips composition and structure of agrocenoses in the forest-steppe zone of the Altai Territory. The territory of the Altai Territory is intensively plowed. Soils are subject to destruction and degradation. The creation of forest belts helps to improve the water regime, increase soil fertility and increase crop productivity. They bind carbon, helping to reduce the greenhouse effect and offsetting industrial carbon dioxide emissions. The role of forest belts in improving the microclimate and reducing wind erosion has been well studied. The changes in the species diversity of weeds and the structure of agrocenoses depending on the structure and species composition of forest belts have not been sufficiently studied. The article presents the results of the study of the influence of poplar forest belts of different designs (2-, 3 - and 4-row) on agrocenoses. The regularities of changes in the height and projective cover of the weed and cultural layer, the composition of weed species, depending on the location of the agrocenosis in relation to the forest belt are revealed. Data on the influence of forest belts of agrophytocenoses located on the southern and northern sides of the forest belt are presented. zanimaetsya diversity of weed species in ugrozenih on megalonyx sites.