Carbon Intensity Research Articles

Carbon footprint of mixed farming crop-livestock rotational-based grazing beef systems using long term experimental data

In the context of ever-growing demand for food and associated concerns regarding the environmental impacts of high-input agricultural systems, there is growing interest in mixed farm enterprises to deliver greater sustainability compared with mono-enterprise production systems. However, assessments of such systems are complex and require high-resolution data to determine the true value and interconnectivity across enterprises. Given the scarcity of information on mixed crop–livestock systems and the difficulties of its analysis, we perform life cycle assessment using temporally high-resolution data (2019–2022) from a long-term experiment in South America to evaluate the ‘cradle-to-farmgate exit’ greenhouse gas emissions intensities of four rotational crop–livestock systems. Systems evaluated were continuous cropping: 2 years of continuous cropping; short rotation: 2-year continuous cropping plus 2-year pasture; long rotation: 2-year continuous cropping followed by 4-year pasture; and forage rotation: continuous pasture. Emissions intensities for beef throughput were reported as kilograms of carbon dioxide equivalents (CO2-eq) per kilogram of liveweight gain (LWG) using the Intergovernmental Panel for Climate Change’s Sixth Assessment Report (AR6 2021) CO2 characterisation factors. Point estimate results were found to be 11.3, 11.8, 11.8 and 16.4 kg CO2-eq/kg/LWG for continuous cropping, short rotation, long rotation and forage rotation, respectively. Emission averages arising from crops, which were separated from animal-based emissions using economic allocation, were 1.23, 0.53 and 0.52 kg CO2-eq/kg for soybean, wheat and oat, respectively. The inclusion of soil organic carbon stock changes had notable effects on reducing each system’s emissions: by 22.4%, 19.2%, 25.3% and 42.1% under continuous cropping, short rotation, long rotation and forage rotation, respectively, when soil organic carbon was included. Given there are few life cycle assessment studies available on such mixed-enterprise ‘semi-circular’ systems, particularly with novel primary data, this study adds critical knowledge to agri-food-related sustainability literature by addressing environmental issues in complex production systems compared to extant and broad coverage of mono-enterprise systems.

Can smart transportation reduce carbon emission intensity? — An empirical study from macro and micro perspectives in China

Traditional transportation development has yielded significant economic and social benefits, but it has also led to increased pollution emissions and substantial environmental impacts. Transport has become a major source of carbon emissions in China. Against the backdrop of national goals for “carbon peak” and “carbon neutrality”, the development of smart transportation offers a fresh perspective on reducing carbon emission intensity. In this study, both provincial and enterprise-level data from China are used to examine the critical role of smart transportation in reducing carbon emission intensity from both macro and micro perspectives, and the dynamic threshold model is employed to validate the inverted U-shaped relationship between the energy structure and digital infrastructure in the carbon reduction process facilitated by smart transportation. In addition, in this mechanism, green innovation and the digital economy play pivotal roles. Multiple robustness tests confirmed the reliability of the empirical analysis. This study highlights the important role of smart transport in carbon intensity and provides valuable insights for developing countries aimed at transitioning toward environmentally friendly, low-carbon, intelligent and advanced transport.

Optimizing green hydrogen production from wind and solar for hard-to-abate industrial sectors across multiple sites in Europe

This article analyzes a power-to-hydrogen system, designed to provide high-temperature heat to hard-to-abate industries. We leverage on a geospatial analysis for wind and solar availability and different industrial demand profiles with the aim to identify the ideal sizing of plant components and the resulting Levelized Cost of Hydrogen (LCOH). We assess the carbon intensity of the produced hydrogen, especially when grid electricity is utilized.A methodology is developed to size and optimize the PV and wind energy capacity, the electrolyzer unit, and hybrid storage, by combining compressed hydrogen storage with lithium-ion batteries. The hydrogen demand profile is generated synthetically, thus allowing different industrial consumption profiles to be investigated.The LCOH in a baseline scenario ranges from 3.5 to 8.9 €/kg, with the lowest values in wind-rich climates. Solar PV only plays a role in locations with high PV full-load hours. It was found that optimal hydrogen storage can cover the users’ demand for 2–3 days.Most of the considered scenarios comply with the emission intensity thresholds set by the EU. A sensitivity analysis reveals that a lower variability of the demand profile is associated with cost savings. An ideally constant demand profile results in a cost reduction of approximately 11 %.

Evaluating direct and diffuse solar radiation components through global radiation measurements from three fixed directions

Solar systems are deemed to play a pivotal role within the process which should lead to the reduction in the carbon intensity of the worldwide energy use. However, for the analysis of solar system performance, the availability of data regarding the various components (direct and diffuse) of solar radiation is crucial.Nevertheless, the measure of either direct or diffuse solar irradiance requires sun tracker apparatuses (pyrheliometers) or shadow devices (e.g. shadow-band pyranometers) which involve mechanical appliances needing constant maintenance and articulated measurement protocols; therefore, these data are usually derived from global solar radiation measured records, by using empirical functions.With a view to trying to address these issues, the article proposes a methodology for the assessment of the direct and diffuse components of solar radiation from both global solar irradiance and infrared radiation data, all measured on three planes differently tilted and oriented.Therefore, no tracking or moving devices are involved, whereas the real configuration and weather conditions of the site (Sky View Factor, clouds, climate conditions, presence of reflective surfaces, etc.) are taken into account. Therefore, the results should possibly characterize the site, so that its actual potential for solar system installation can be properly assessed.Comparisons with different correlation models demonstrate good agreement, showing correlation coefficients ranging from 0.75 to 0.89 for diffuse radiation and from 0.97 to 0.99 for direct radiation. Notably, the proposed method tends to produce lower values for diffuse radiation. These discrepancies may be due to the peculiar features of the site.

An electricity-driven mobility circular economy with lifecycle carbon footprints for climate-adaptive carbon neutrality transformation

Under the carbon neutrality targets and sustainable development goals, emergingly increasing needs for batteries are in buildings and electric vehicles. However, embodied carbon emissions impose dialectical viewpoints on whether the electrochemical battery is environmentally friendly or not. In this research, a community with energy paradigm shifting towards decentralization, renewable and sustainability is studied, with multi-directional Vehicle-to-Everything (V2X) and lifecycle battery circular economy. Approaches are proposed to quantify the lifecycle carbon intensity of batteries. Afterwards, pathways for zero-carbon transformation are proposed to guide the economic feasibility of energy, social and governance investment behaviors. Results show that lifecycle zero-carbon battery can be achieved under energy paradigm shifting to positive, V2X interaction, battery cascade utilization and battery circular economy in various climate regions. This study proposes an approach for lifecycle battery carbon intensity quantification for sustainable pathways transition on zero-carbon batteries and carbon-neutral communities.

Unlocking the nexus: exploring the effect of urban green innovation on haze pollution and carbon emissions intensity in Chinese cities

Unlocking the nexus: exploring the effect of urban green innovation on haze pollution and carbon emissions intensity in Chinese cities

Carbon savings from sugarcane straw-derived bioenergy: Insights from a life cycle perspective including soil carbon changes

Sugarcane straw removal for bioenergy production will increase substantially in the next years, but this may deplete soil organic carbon (SOC) and exacerbate greenhouse gas (GHG) emissions. These aspects are not consistently approached in bioenergy life cycle assessment (LCA). Using SOC modeling and LCA approach, this study addressed the life cycle GHG balance from sugarcane agroindustry in different scenarios of straw removal, considering the potential SOC changes associated with straw management in sugarcane-cultivated soils in Brazil. Long-term simulations showed SOC losses of up to −0.5 Mg ha−1 yr−1 upon complete straw removal, whereas the moderate removal had little effects on SOC and the maintenance of all straw in the field increased SOC accumulation by up to 0.4 Mg ha−1 yr−1. Our analysis suggests that accounting for SOC changes in LCA calculations could lower the net GHG benefits of straw-derived bioenergy, whose emissions intensity varied according to soil type. Overall, SOC depletion induced by complete straw removal increased the life cycle GHG emissions of straw-derived bioenergy by 26 % (3.9 g CO2eq MJ−1) compared to a scenario without taking SOC changes into account. Straw removal for cellulosic ethanol could be effective for mitigating GHG emissions relative to gasoline, but it was not advantageous for bioelectricity generation depending on the energy sources that are displaced. Therefore, straw-induced change of SOC stocks is a critical factor to model life cycle GHG emissions of straw-derived bioenergy.

Data collection framework for enhanced carbon intensity indicator (CII) in the oil tankers

AbstractThe International Maritime Organization (IMO) aims to reduce greenhouse gas (GHG) emissions by 40% by 2030 compared with 2008. The carbon intensity indicator (CII) calculates the annual reduction factor required to continuously improve a ship's operational carbon intensity at a specific rating level. Verification and documentation of the achieved annual operational CII against the prescribed target are necessary to establish the operational carbon intensity rating. This study focuses on the intricate process of data collection for CII within the oil shipping industry, targeting engineering departments and shipboard management teams. Against the backdrop of the industry's substantial carbon dioxide emissions, the IMO has mandated the calculation of CII values for ships exceeding 5000 gross tons to promote sustainability and reduce environmental impact. We have collected emission data of 20 oil tankers over a period of 2 years using our ship maintenance and operating system (SMOS) and analyzed the data to compare the CII ratings. Our results indicate that a staggering ~63% of the vessels had the lowest CII rating of category E. It is therefore crucial to properly collect, organize, and evaluate data for CII calculation and take necessary measures to improve rating. This paper provides a deeper insight into the evolving CII calculation methodology, emphasizing the incorporation of correction factors and exclusions, and delineates the essential data collection practices needed to facilitate accurate CII calculations.

The Indirect Carbon Cost of E-Mobility for Select Countries Based on Grid Energy Mix Using Real-World Data

Electric vehicles are considered by many as an emission-free or low-emission solution to meet the challenge of sustainable transportation. However, the operational input, electrical energy, has an associated cost, greenhouse gasses, which results in indirect emissions. Given this knowledge, we pose the following question: “Are zero-emission transportation targets achievable given our current energy mix?” The objective of this article is to assess the impact of a grid’s energy mix on the indirect emissions of an electric vehicle. The study considers real-world data, vehicle usage data from an electric vehicle, and carbon intensity data for India, the USA, France, the Netherlands, Brazil, Germany, and Poland. Linear programming-based optimization is used to compute the best charging scenario for each of the given grids and, consequently, the indirect emissions are compared to those of a high-efficiency 1.5 L diesel internal combustion engine for the vehicle: a 2019 Renault Clio dCi 85. The results indicate that for grids with low renewable energy penetration, such as those of Poland and India (Maharashtra), an electric vehicle, even when optimally charged, can be classified as neither a low- nor zero-emission alternative to normal thermal vehicles. Also, for grids with elevated levels of variation in their carbon intensity, there is significant potential to reduce the carbon footprint related to charging an electric vehicle. This article provides a real-world perspective of how an electric vehicle performs in the face of different energy mixes and serves as a precursor to the development of robust indicators for determining the carbon reductions related to the e-mobility transition.

A bi-level model for coal power decarbonization via biomass co-firing considering CO[formula omitted] emission trading system

Faced with the challenges posed by the Paris Agreement targets, biomass co-firing offers an efficient measure to decarbonize coal-fired power generation. Although economic burden of biomass co-firing has severely limited the large-scale application, effective CO2 emission trading system has the capability to subsidize and incentivize. Existing studies related to biomass co-firing under CO2 emission trading system fail to consider multiple decision makers and uncertain decision-making environment. To address these limitations, this paper proposes a bi-level model for coal power decarbonization via biomass co-firing considering CO2 emission trading system in an uncertain decision-making environment. It considers the interactive relationship between multiple decision makers. The upper-level model focuses on the formulation of CO2 emission trading system, and the lower-level model decides biomass co-firing and emission reduction. Fuzzy set theory is employed to describe the uncertain parameters and convert them into exact values. To calculate the model, a bi-level interactive method based on satisfactory solution is developed. A case study is conducted to illustrate the effectiveness and practicality of the proposed model. Results show that the price of initial quotas fluctuates between 38.01 and 48.7 CNY/tonne, and the lowest carbon intensity is 0.741 kg/kWh. Management recommendations are provided to support CO2 emission reduction.

Integrating the land–water–climate nexus approach with spatial-based sustainable agriculture production

CONTEXTA policy on agricultural land zoning, based on land suitability maps, has been introduced to optimize the use of resources for crop production in Thailand. However, the impact of activities occurring during cultivation in different land suitability classes has not been considered. A land–water–climate nexus index (LWCNI) has therefore been introduced as a holistic analysis that integrates three indicators––land-use intensity, water-use intensity, and climate change impact in terms of GHG emissions. This can guide resource allocation by highlighting areas that need improvement. OBJECTIVEWe aimed to assess the land–water–climate nexus in different land-suitability classes for sugarcane and cassava in Thailand in order to compare the nexus performances and improve the planning and management of resources for sustainable production. METHODSThe LWCNI was developed and assessed by combining it with geographic information system–based tools for evaluating the nexus performances of sugarcane and cassava cultivation as per the agricultural zoning policies. The scope of the work was “from cradle to farm-gate”, and the final products considered were sugarcane and fresh cassava. RESULTS AND CONCLUSIONSThe results revealed that sugarcane cultivation in moderate-suitability areas is the most effective, with the best LWCNI score being 0.87, suggesting efficient cultivation practices with a minimal use of land and water resources, and a minimal climate change impact. The three indicators of land-use intensity, water-use intensity, and GHG emissions for sugarcane were estimated at 0.010 ha/t, 108 m3/t, and 6.2 kg carbon dioxide equivalent (CO2e)/t, respectively. Cassava cultivation in the high-suitability area was the most efficient, with the best LWCNI score, at 0.95, and using less land, fewer water resources, and having less of a climate change impact. The land-use intensity, water-use intensity, and GHG emissions of cassava were estimated at 0.030 ha/t, 255 m3/t, and 35.7 kgCO2e/t, respectively. Growing crops in marginal and unsuitable areas presented a risk of a high climate change impact as well as competition for water resources due to limited local resources. We found that, even in the same land-suitability class, the amount of fertilizer used by the farmers varied depending on their knowledge, experience, and finances. Excessive fertilizer application increased GHG emissions, which in turn brought about a decrease in the LWCNI score, leading to a poorer nexus performance. SIGNIFICANCEThe findings emphasize the relationship between land and water use and climate change impact across varying spatial contexts. The LWCNI approach offers valuable insights for supporting agricultural zoning policy.

Exporting, Abatement, and Firm-Level Emissions: Evidence from China’s Accession to the WTO

Abstract This paper studies the joint impact of exporting and abatement on the environmental performance of Chinese manufacturers. For two common air pollutants (SO2 and industrial dust) we document that (a) exporters are significantly less emissions-intensive relative to their nonexporting counterparts and (b) this difference cannot be explained by differential rates of abatement alone. Employing variation in trade and environmental conditions across time and space, we quantify the impact of endogenous export and abatement decisions on firm-level emissions. We find that exporting reduces emissions by at least 36% across pollutants. We explore underlying determinants of export-driven reductions in emissions intensity.

Economic growth and income inequality increase the carbon intensity of human well-being for Canada’s provinces

Reducing the carbon intensity of human well-being (CIWB) is a potential form of climate action. We conduct a preliminary analysis of the effects of economic growth and income inequality on the CIWB of Canada’s provinces, and find that both increase CIWB in this sub-national context. We also find that their effects are symmetrical, meaning that positive and negative changes in economic growth and income inequality result in the same proportional changes in CIWB. Therefore, and while incredibly difficult to do, it is possible that efforts to reduce income inequality and economic growth are potential pathways to reducing CIWB.

A Distributed Computational Model for Estimating the Carbon Footprints of Companies

A new approach based on input–output (IO) analysis has emerged to estimate the carbon footprints of companies and their products from cradle to gate. While the approach relies on the same principles as the GHG Protocol, it uses a distributed iterative framework to improve the footprint estimations and reduce their uncertainty. While optimal estimations would result if all the world’s companies would enter such a system, this paper shows how such a distributed system could apply to the real world where many enterprises would stay out of the system. We show how the quality of the estimations with respect to the GHG Protocol would be increased by integrating scope 1 and scope 2 data from the value chains in the footprint estimations and progressively reducing the part of the remaining scope 3 data. To help with analyzing uncertainty, we show how to use the scope 1/2/3 decomposition to estimate the biases and the standard deviations of the computed production carbon intensities. We illustrate the model on macroeconomic data for 44 sectors and two regions (Europe and Rest of World), using the Inter-Country Input–Output database from the OECD. Such a system would necessarily rely on Information and Communication Technology, since the companies would be permanently interconnected in a large-scale meshed network, using an application protocol for data exchange.

Energy management of airport service electric vehicles to match renewable generation through rollout approach

Traditional diesel-based airport service vehicles are characterized by a heavy-duty, high-usage-frequency nature and a high carbon intensity per vehicle per hour. Transforming these vehicles into electric vehicles would reduce CO2 emissions and potentially save energy costs in the context of rising fuel prices, if a proper energy management of airport service electric vehicles (ASEVs) is performed. To perform such an energy management, this paper proposes a new customized rollout approach, as an optimal control method for a new ASEV dynamics model, which models the ASEV states, their transitions over time, and how control decisions affect them. The rollout approach yields an optimal control strategy for the ASEVs to transport luggage and to charge batteries, with the objective to minimize the operation cost, which incentivizes the charging of the ASEVs to match renewable generation. Case studies demonstrate that the rollout approach effectively overcomes the “curse of dimensionality” challenge. The rollout algorithm results in a total cost approximately 10 % less than that of the underlying “greedy charging” heuristic, which charges a battery whenever its state of charge is not the maximum. The rollout algorithm is proven to be adaptive towards flight schedule changes at short notice.

Can place-based policy reduce carbon emissions? Evidence from industrial transformation and upgrading exemplary zone in China

The effect of the place-based policies on sustainable development has received substantial attention in economic research. In China, the industrial transformation and upgrading exemplary zone policy is a prominent example of such policies, as it targets old industrial and resource-based cities. The paper provides an early assessment of the policy’s capability to reduce carbon emissions. Specifically, we use data from old industrial and resource-based cities for the period of 2012–2019 and apply the difference-in-differences method to examine the policy’s influence on total carbon emissions and intensity. The results show that the policy can effectively reduce carbon emissions through reducing energy consumption, promoting urban green innovation and tertiary industry agglomeration. Compared to resource-based cities, cities with a low level of economic development and central, western cities, this impact is more notable in old industrial cities, cities with a high level of economic development and eastern cities. Additional analysis reveals that the policy has beneficial spatial radiation impacts on the nearby cities as well. In the meanwhile, the strategy may have a synergy effect on reducing carbon emissions and pollution. The results of this study may have an impact on how nations implement place-based policies and reduce carbon emissions.

Greenhouse gas emissions and nutrient use efficiency assessment of 6 New York organic dairies

Improving nutrient use efficiency and reducing GHG emissions are important environmental priorities for organic-certified dairy operations. The objectives of this research were to quantify annual nutrient use and GHG emissions in 6 organic New York dairy farms. Farm-gate nutrient mass balances (NMB) were estimated with the Cornell NMB calculator. Whole-farm GHG emissions were estimated using the Cool Farm Tool (CFT) and COMET. Farm-gate NMB were low, ranging from -6.5 to 19 kg N/ha for N1 (without legume N fixation), 26 to 71 kg N/ha for N2 (including N fixation), -2.4 to 8.2 kg P/ha for P, and 1.1 to 19.8 kg K/ha for K. Additional nutrient imports, coupled with nutrient management planning, adequate legume stands, and diet balancing may help improve P balances and ensure no N deficiencies in the system. Estimates of annual GHG emissions intensity ranged from 0.98 to 2.10 kg of CO2-equivalents (CO2-eq) per kilogram of fat- and protein-corrected milk (FPCM) estimated by CFT, and from 0.69 to 2.48 kg CO2-eq/kg FPCM estimated by COMET. Enteric fermentation, feed production, and fuel and energy use represented the largest sources of GHG. For farms with liquid manure storages, manure management was also a significant source. Estimates of soil carbon (C) stock changes from CFT were in agreement with or smaller than previous studies, and estimates from COMET were in agreement or greater. Variability and uncertainty in the results for soil C stock change indicate that more research and new protocols are needed. The effects of individual management changes on GHG emissions intensity were small, ranging from -8 to +7% in CFT, and -8% to +8% in COMET. The management changes that resulted in the largest reductions in GHG emissions intensity included increasing individual cow productivity and ratio of milk to total feed, and implementation of manure treatment systems.

Mechanisms for implementing fossil fuel divestment in portfolio management with impact on risk, return and carbon reduction

Mechanisms to incentivize divestment strategies, such as divestment schedules, are an important component of carbon reduction strategies. We use dynamic asset allocation methodologies to assess this impact over time on index portfolios (S&P 500 and FTSE 100), and global exchange-traded funds (ETFs). Although return profiles are not affected, the risk profile of S&P 500 divestment portfolios is impacted by rapid divestment strategies as divestment concentration increases. Instantaneous divestment may benefit management structure, while slower divestment provides greater stability in portfolios’ tracking errors and benefits carbon reduction, especially from reinvested capital. Divesting from energy and utilities sectors reduces carbon footprint of up to 7%, while ETFs’ divesting from highly carbon concentrated ETFs offers further carbon footprint reductions. Investing in funds with low carbon footprint results in lower dividend returns and management fees. Although ETFs’ returns are insensitive to divestment strategies and schedules, their risk profiles are affected, proportionally to their carbon intensity, especially for rapid divestment and at the expense of higher tracking errors. Divestment strategies based on ESG rating screening of FTSE 100 portfolios improve diversification and impact risk/return performance. Our study underscores the importance of considering investors’ demographics, such as dividends, management structure, and carbon reduction targets.

Assessment and Driving Factors of Embodied Carbon Emissions in the Construction Sector: Evidence from 2005 to 2021 in Northeast China

Reducing embodied carbon emissions in the construction sector is pivotal for achieving sustainable development goals, mainly those related to health and well-being, sustainable cities and communities, and climate action. Hence, it is crucial to delve into the trends and influencing factors of construction-embodied carbon, especially in countries like China, where extensive construction projects are underway. Previous studies have investigated carbon emissions at both national and regional levels, whereas research specific to the construction sector in Northeast China remains limited. This study assessed the embodied carbon of the construction sector in Northeast China from 2005 to 2021. The results indicated that embodied carbon initially rose before declining, peaking at 278.9 MtCO2e in 2012. Based on the Logistic Mean Divided Index (LMDI) approach, the variations in embodied carbon were decomposed into seven driving factors, including emission source structure, embodied carbon intensity, construction mechanization, machinery requirement, industrial structure, economic development, and population scale. While construction mechanization and economic development were identified as primary drivers of the changes in embodied carbon, carbon emission intensity and population scale exerted inhibiting effects on the rise. Moreover, potential strategies for mitigating construction-embodied carbon in Northeast China were delineated, underscoring the regionality across different provinces. The results and suggestions can help foster a low-carbon construction industry from a provincial perspective.

Unveiling the impact of industrial robots on consumption-based embodied carbon intensity: A global perspective

With the widespread application of industrial robots, exploring the patterns of consumption-based embodied carbon intensity is crucial for understanding the fairness of carbon emissions responsibility and supporting global sustainability efforts. Employing the dynamic panel Generalized Method of Moments estimation methodology to a dataset including 52 countries, this paper evaluates the influence of the applications of industrial robot on the consumption-based embodied carbon intensity and its corresponding mechanisms. The main findings reveal that: (i) the application of industrial robots significantly reduces consumption-based embodied carbon intensity. The result remains robust after undergoing a series of robustness checks. (ii) In countries with greater advantages in environmental technology and a higher abundance of natural resources, the application of industrial robots results in more noticeable environmental benefits. (iii) While the impact of industrial robots on consumption is complex, their implementation boosts human capital and reduces income inequality, thereby lowering carbon intensity. The findings highlight the importance of human capital and reduced inequality in promoting eco-friendly consumption behaviors. Furthermore, the adoption of industrial robots has driven green innovation, enhancing environmental quality from the supply side. The findings provide policy implications for fostering global low-carbon transformation using industrial intelligence approaches.