Emission Reduction Policies Research Articles

A dual-core system dynamics approach for carbon emission spillover effects analysis and cross-regional policy simulation

As carbon emission continue to rise and climate issues grow increasingly severe, countries worldwide have taken measures to reduce carbon emission. However, carbon dioxide is continuously flowing in the atmosphere and is easily influenced by neighboring cities' policies. Therefore, how to solve the problem of carbon emission spillover effect has become the key to improve policy efficiency. Cross-regional carbon governance provides a perspective on solving the carbon emission problem by regulating and guiding the cooperative behavior of cross-regional governance actors. Taking Chengdu-Chongqing area as an example, this study used the SDM to analyze the influencing factors and spatial spillover effects of emission. Then we used the system dynamics method to construct a dual-core carbon emission system, and simulated the spillover effect and emission reduction potential of Chengdu and Chongqing emission reduction policies under different policy schemes. The results reveal that the mobility of population and enterprises have a significant impact on carbon emission prediction. Carbon reduction policies exhibit the phenomena of "carbon transfer" and "free-riding." When Chengdu lowers its economic growth rate, it leads to the transfer of high energy-consuming enterprises to Chongqing, increasing carbon emission in Chongqing. The implementation of comprehensive carbon reduction policies in Chongqing has a positive effect on Chengdu. Emission reduction policies exhibit issues related to their temporal efficacy, as the effects of industrial structural policies in Chengdu yield opposite outcomes in the short and long term. Each city's unique circumstances necessitate tailored carbon reduction policies. In order to reduce carbon emissions, Chengdu and Chongqing require opposite population policies.

Multi-stage two-echelon crowdsourcing logistics assignment model with future committing drivers

With the popularity of low-carbon economy, governments of all countries have introduced energy-saving and emission reduction policies. Crowdsourcing logistics, a logistics assignment model developed under the sharing economy, can effectively use transport resources from the public and thus meaningfully contribute to sustainability. It has important practical significance both for energy saving and emission reduction in last-mile logistics distributions, and hence plays an important role in tackling the challenges associated with last-mile and same-day deliveries. This paper investigated the crowdsourcing logistics task assignment problem where logistics platforms can perform multi-stage task assignment. The information of all tasks to be delivered in each stage is known a priori, however the information of crowdsourcing drivers in future stages is not known completely. A multi-stage two-echelon dynamic task assignment model (MS-2E-DAM) was developed for the problem and a heuristic which combines genetic algorithm and tabu search (GA-TS) was developed. Its performance was benchmarked with CPLEX 12.10 for small-size problems and the results demonstrated the effectiveness of the proposed heuristic approach. For large-size problems, the proposed approach can help reduce the overall cost by 1.94% over traditional assignment approaches. Sensitivity analyses on three key parameters helped identify the key factors that affect the system cost, and several management suggestions were proposed based on the results.

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

The environmental damage caused by the greenhouse effect is intensified by the continuous rise of carbon dioxide emissions. As a major industrial base in northern China, Tianjin has been emitting more carbon and thus faces a greater challenge to reduce its emissions. To meet the dual carbon goals of Tianjin, it is critical to identify influencing factors of carbon emissions and to find suitable technologies to effectively reduce emissions. In this study, the energy consumption data of Tianjin from 2005 to 2020 were analyzed via the Logarithmic Mean Divisia Index (LMDI). The carbon emission driving factors were decomposed and the contributions of four primary factors, including energy structure, energy intensity, gross domestic product (GDP) per capita, and population to CO2 emissions of this region, were evaluated. The ideal characteristics of the oil reservoirs with low permeability and the suitable carbon dioxide sinks make carbon capture and storage(CCS) technology implementation feasible in Tianjin. Despite this, the effect of CCS technology on CO2 emissions in the future is still uncertain. Thus, the Bass model was firstly introduced to assess the future development trend of CCS technology and its capacity of CO2 emission reduction. The results revealed that energy intensity was the primary contribution to reducing carbon emissions, while GDP per capita was the main contributor to the overall increase of CO2 emissions in 2005–2020. The CCS technology diffusion process is fast, with rapid growth starting in 2030 and reaching growth saturation in 2040. According to the scenario analysis, CCS could make a substantial contribution of 2674.78× 104 tons per year after 2040 to the carbon emission reduction of Tianjin. Therefore, the implementation of energy conservation and emission reduction policies as well as the expansion of CCS projects are essential to ensure that Tianjin achieves its carbon neutrality goal by 2060. This study offers helpful guidance for other cities to forecast their carbon emission reduction trajectories and establish emission reduction strategies.

Study on the coupling coordination development of China's multidimensional digital economy and industrial carbon emission efficiency.

Exploring the coupling coordination between China's digital economy (DE) and industrial carbon emission efficiency (ICEE) is of great significance for achieving sustainable development goals. In the study, a multidimensional indicator system was established to evaluate DE, and spatiotemporal analysis and network analysis methods were used to reveal the dynamic evolution characteristics of DE and ICEE. The coupling coordination model and convergence model were adopted to explore the development trend of coupling coordination between DE and ICEE. The results show that the ICEE and DE in various provinces of China exhibit obvious spatial heterogeneity and spillover effects. Currently, the coupling coordination degree between the development of China's DE and ICEE has reached the level of primary coordination or above. The coupling coordination degree between DE and ICEE in the eastern, central, and northeastern regions has reached an intermediate level or above, with the highest degree in the eastern region. The fluctuation of China's ICEE has consistent σ-convergence and β-convergence, and the convergence effect is higher with the introduction of the DE than without it. The condition β-convergence result indicates that underdeveloped regions can narrow the gap between their ICEE and that of developed regions by utilizing their resource endowments, industrial structure, human capital, and other conditions, improving emission reduction measures and policies. This study provides a certain reference for the green and low-carbon development of industry in China and other developing countries in the digital economy era.

Statistical data-based prediction of carbon dioxide emission factors of China's power generation at carbon peak in 2030

China has promised to reach its peak carbon dioxide emissions by 2030. Power generation emits the most carbon dioxide in China. Fossil fuels are currently still the largest sources of energy for electricity generation in China. As the most commonly used method for estimating emissions from thermal power generation, emission factor data has a statistical lag, which brings inconvenience to carbon emission accounting and the formulation of energy conservation and emission reduction policies. It is of great significance to predict the emission factors of fossil fuel-fired power generation in China. In this paper, the correlations between the data of coal-fired, gas-fired, and oil-fired power generation from 2012 to 2019 and the emission factors of China's power generation and the correlations between the data of fossil fuel-fired power generation from 2006 to 2019 and the emission factors of China's power generation are evaluated using SPSS. On this basis, two models for predicting emission factors of China's power generation from 2020 to 2030 are established and the relevant data are obtained. The calculation results show that when the carbon peak reaches in 2030, the emission factor of China's power generation may be 0.6290 and 0.7363 corresponding to the two prediction models.

Climatological assessment of the vertically resolved optical and microphysical aerosol properties by lidar measurements, sun photometer, and in situ observations over 17 years at Universitat Politècnica de Catalunya (UPC) Barcelona

Abstract. Aerosols are one of the most important pollutants in the atmosphere and have been monitored for the past few decades by remote sensing and in situ observation platforms to assess the effectiveness of government-managed reduction emission policies and assess their impact on the radiative budget of the Earth's atmosphere. In fact, aerosols can directly modulate incoming short-wave solar radiation and outgoing long-wave radiation and indirectly influence cloud formation, lifetime, and precipitation. In this study, we quantitatively evaluated long-term temporal trends and seasonal variability from a climatological point of view of the optical and microphysical properties of atmospheric particulate matter at the Universitat Politècnica de Catalunya (UPC), Barcelona, Spain, over the past 17 years, through a synergy of lidar, sun photometer, and in situ concentration measurements. Interannual temporal changes in aerosol optical and microphysical properties are evaluated through the seasonal Mann–Kendall test. Long-term trends in the optical depth of the recovered aerosol; the Ångström exponent (AE); and the concentrations of PM10, PM2.5, and PM1 reveal that emission reduction policies implemented in the past decades were effective in improving air quality, with consistent drops in PM concentrations and optical depth of aerosols. The seasonal analysis of the 17-year average vertically resolved aerosol profiles obtained from lidar observations shows that during summer the aerosol layer can be found up to an altitude of 5 km, after a sharp decay in the first kilometer. In contrast, during the other seasons, the backscatter profiles fit a pronounced exponential decay well with a well-defined scale height. Long-range transport, especially dust outbreaks from the Sahara, is likely to occur throughout the year. During winter, the dust aerosol layers are floating above the boundary layer, while during the other seasons they can penetrate the layer. The analysis also revealed that intense, short-duration pollution events during winter, associated with dust outbreaks, have become more frequent and intense since 2016. This study sheds some light on the meteorological processes and conditions that can lead to the formation of haze and helps decision makers adopt mitigation strategies to preserve large metropolitan areas in the Mediterranean basin.

Comparative Analysis of Trade’s Impact on Agricultural Carbon Emissions in China and the United States

Accelerating economic globalization is a major driver of the transfer of embodied pollutant emissions from trade. China and the United States are currently the largest importers and exporters of agricultural products, respectively, and are also major producers and consumers of these products. This paper aims to analyze and compare the patterns of embodied agricultural carbon emissions (ACE) in the two countries, which is crucial for understanding how trade influences the transfer of such emissions. In this study, we calculated the embodied ACE of China and the United States from the perspectives of production and consumption for the years 1970, 1980, 1990, 2000, 2010, and 2016 by establishing a multi-regional input–output (MRIO) model. Additionally, we employed the Logarithmic Mean Divisia Index (LMDI) decomposition method to analyze the driving factors behind the changes in embodied ACE over time. The findings indicated that the embodied ACE associated with imports and exports in China and the United States followed a pattern of increase and subsequent decrease during the period 1970–2016, with net imports escalating from −18.79 million tons and −3.62 million tons to 40.35 million tons and 51.22 million tons, respectively. This study identified two main factors contributing to the reduction in embodied ACE in both countries: the declining intensity of embodied ACE per unit of traded products and the diminishing proportion of the primary industry. The growth in GDP per capita, population expansion, and an increase in the proportion of agricultural products in international trade are predicted to promote an increase in embodied ACE imports and exports in both countries. This paper advocates for the reduction of embodied ACE through the continuous promotion of research and application of energy-saving and emission-reduction technologies, an optimized industrial structure, and the implementation of relevant energy-saving and emission-reduction policies.

Effect of Carbon Dioxide Emission Reduction Policy on Air Quality Improvement in Jiangsu Province

Carbon emission peaking and air quality improvement is an urgent issue in the research of the atmospheric environment. Here, the emission factor method was used to compile the city-level greenhouse gas emission inventory of Jiangsu Province from 2010 to 2019, which was then combined with greenhouse gas-air pollutant synergy analysis and WRF-Chem air quality model simulation to analyze the synergistic gain of air quality improvement under different carbon emission reduction scenarios. The results revealed that the annual mean CO2 emission in Jiangsu Province from 2010 to 2019 was 701.74-897.47 Mt. Suzhou, Xuzhou, and Nanjing had the highest emissions (91.19-182.12 Mt·a-1); Yangzhou, Suqian, and Lianyungang had the lowest emissions (13.19-32.54 Mt·a-1); and majority of the cities had a continuous upward trend in the CO2 emissions. Energy activities were the main source of CO2 emissions, accounting for nearly 90%, whereas industrial production processes contributed to the remaining 10%. This study designed three types of CO2 emission reduction conditions according to different emission reduction priorities, namely, sector-wide collaborative, energy priority, and industrial priority. Each type of emission reduction condition included a different intensity of CO2 emission reduction (10%, 20%, and 40%). The condition-based simulation results demonstrated that, taking 2017 as the base year, the average annual decrease in PM2.5 concentration in sector-wide collaborative, energy priority, and industrial priority emission reduction was 6.7-21.1, 3.1-12.0, and 3.4-14.3 μg·m-3, respectively. Sector-wide collaborative emission reduction had the most notable improvement in PM2.5 pollution. Under the condition of the sector-wide collaborative emission reduction of 40%, the average annual PM2.5 concentration of all cities, excluding Xuzhou and Suqian, met the national Ⅱ standard (35 μg·m-3). The change responses of PM10, SO2, NO2, and CO were similar to that of PM2.5, but O3 pollution increased under the conditions of energy and industrial priorities.

Long-term historical trends in air pollutant emissions in South Korea (2000–2018)

This study aims to understand the impact of previous air quality improvement policies on historical emission changes by examining long-term emission trends in Korea. Annual emissions from 2000 to 2018 were estimated using Korea’s official emissions inventory, the Clean Air Policy Support System (CAPSS). To ensure a consistent comparison, standardization of the method for calculating emissions and unification of the reported emission sectors were conducted each year. Furthermore, Korea’s emissions history was compared with that of neighboring countries, such as China and Japan. The annual emissions of these countries were acquired from the HTAPv3 emissions inventory, an international long-term emission trend study. For comparison, the emission source classification of Korea was matched with that of HTAPv3. As a result of the analysis, NOx and SOx emissions in Korea have shown decreasing trends, whereas VOCs (volatile organic compounds) have indicated a gradual increasing trend since 2000. Compared to the previous period of implementing South Korea’s air quality improvement policy, changes in NOx and SOx emissions, which are combustion-related pollutants, showed a relationship with the policy’s timeline. However, non-combustion-related pollutants such as VOCs did not exhibit such a relationship. It was concluded that the related policies were not as effective in reducing VOCs as planned in the policy. By comparing the emission trends of Japan, Korea, and China, it was confirmed that Japan was the first country to experience a decrease in combustion-related pollutants emissions, followed by Korea and China. Additionally, combustion-related pollutants decreased in all three countries, whereas VOCs decreased only in Japan. VOC is a precursor material generating secondary PM2.5 and Ozone; considering that, if relevant policies are additionally implemented to control future PM2.5 concentrations, and to reduce emissions efficiently and effectively, Japan’s VOC reduction policies can be applied to Korea’s emission reduction policies. These results are expected to serve as important references when establishing future air quality improvement policies in Korea.

Plants' emission behaviors under dual control of pollutant concentration and quantity

Quantity-based and concentration-based limits are two common environmental permitting approaches utilized by government worldwide in environmental management. While existing literature is still unambiguous about roles played by quantity-based versus concentration-based limits in environmental management, it becomes evident that relying exclusively on concentration-based or quantity-based limits to control plant emissions may not necessarily result in improved environmental quality. This paper leverages a unique opportunity arising from a recent reform in China's Pollutant Emission Permit System (PEPS) initiated in 2016 to analyze how the introduction of quantity-based limits in addition to concentration-based limits through the PEPS reform impact emissions at the plant level. Utilizing a unique plant-level continuous emission monitoring system data collected from Shaanxi Province (located in western China), the paper finds a significant reduction in air pollutant emissions as a result of the PEPS reform (nitrogen oxides (NOx) by 39%, sulfur dioxide (SO2) by 15% and particulate matter (PM) by 13%). The heterogeneity analyses show emission reductions in plants differ across those with varying quantity limits specified in their permits, distinct emission ratios and diverse ownership structures. Furthermore, plants that fall under the classified management system with more stringent regulations imposed, especially those operating in high-pollution sectors, situated within industrial parks, or classified as large-sized plants, attain higher pollutant quantity limits. Findings of the paper carry important implications for effective environmental management, particularly within developing countries, and shed some light on carbon emission reduction policies in China.

A novel fractional-order grey prediction model: a case study of Chinese carbon emissions.

Objective and accurate prediction of carbon emissions can provide a basis for the country to achieve carbon emission reduction targets and can also comprehensively and scientifically predict the peak of carbon emissions effectively, providing valuable reference information for the implementation of specific emission reduction policies and measures at each stage. In this paper, a novel fractional-order grey multivariate forecasting model is established to analyze and forecast China's carbon emissions, reflecting the principle of new information priority. The model adds fractional-order cumulative sequences to the traditional integer-order cumulative sequences, uses the Gamma function to represent the fractional-order sequences and the time-response equation, and uses the particle swarm algorithm to find the optimal order of the cumulative sequence. Finally, the modeling steps of the model are given. Then, the new model is analyzed for its effectiveness from three different perspectives using 21years of Chinese carbon emission data. The results of the first and second cases show that the newly established particle swarm optimization fractional-order model is superior to the grey multivariate comparison model. The results of the third case show that the new model is superior to the three classical grey prediction comparison models. It has stable characteristics for both simulation and prediction and also shows high accuracy, and all three cases fully illustrate the effectiveness of the new model. Finally, this new model is applied to forecast China's carbon emissions from 2022-2026, analyze the forecast results, and make relevant recommendations.

High-quality economic growth and carbon emissions in Chinese cities: the moderating role of fiscal policies

In the rapidly evolving landscape of contemporary China, urban centers have emerged as focal points of a significant environmental challenge—carbon emissions. This comprehensive study delves into an intricate analysis, utilizing data gathered from 140 prefecture-level cities across China. Its principal aim is to dissect the effectiveness of strategies aimed at carbon reduction and fiscal policies within the multifaceted canvas of China’s urban metamorphosis, where the pursuit of high-caliber economic development takes precedence. The findings can be succinctly summarized as follows. Firstly, a statistically significant inverse correlation exists between high-quality economic development and carbon emissions in China’s urban centers. Secondly, when comparing resource-based cities to their non-resource-based counterparts, the former’s high-quality economic development plays a more prominent role in fostering carbon emission reduction. Finally, fiscal policies emerge as pivotal “accelerators” for advancing carbon emission reduction through high-quality economic development. However, their efficacy exhibits notable variations. It is essential to note that the moderating effect of environmental protection expenditures lacks statistical significance in resource-based cities and northern cities. Furthermore, the regulatory influence of resource taxation in southern cities is yet to be firmly established. This study provides practical policy recommendations for optimizing China’s eco-friendly fiscal system. These recommendations not only contribute to the realization of a green transition model for economic development but also serve as a valuable reference for governmental design of carbon emission reduction policies.

The underlying drivers of environmental pollution: the role of semi-urbanization

ABSTRACT Assessing the impact of the driving forces on environmental pollution has a significant theoretical value for the construction of high-quality economic development. However, to our knowledge, no past study considers how semi-urbanization impacts the environment. This study provides empirical evidence on environmental pollution in China from the perspective of semi-urbanization. Also, the underlying mechanisms of semi-urbanization affecting environmental pollution are explored. The estimation results indicate that semi-urbanization shows prominent spatial agglomeration. We also observed that semi-urbanization in China can achieve a meaningful reduction of environmental pollution. Further, semi-urbanization negatively influences environmental pollution in the local region, but it contributes to environmental pollution growth in neighboring regions. In numerical terms, for every 1% rise in semi-urbanization of the province leads to a decrease in emissions by around 0.19% locally and by around 0.30% in neighboring regions; every 1% rise in semi-urbanization of the province decrease SW emissions by around 0.28% locally and increase by around 0.29% in neighboring regions. The investigation above helps policymakers to formulate effective emission reduction policies for realizing the “win-win” situation of promoting semi-urbanization and curbing environmental pollution.

Co-benefits of policies to reduce air pollution and carbon emissions in China

Coordinated efforts to reduce pollution and carbon emissions have become an inevitable choice for China's comprehensive green transformation of economic and social in the new stage of development. However, the co-benefits of policies to reduce air pollution and carbon emissions in different departments has not been effectively evaluated. This study uses the Multi-resolution Emission Inventory model for Climate and air pollution research (MEIC) to calculate the carbon and major air pollutants emission tracks at the provincial and sectoral levels in China from the bottom to up. We evaluate the synergies between current pollution reduction policies and carbon reduction policies in the power, industry, transport, resident and agriculture sector. The results show that the implementation of current emission reduction policies has significantly reduced major air pollutants and slowed down the growth rate of CO2 emissions. The synergistic effects of the carbon reduction policy and pollution reduction policies are various in different sectors, and pollution reduction policies have a greater inhibitory effect on controlling air pollution and carbon emission. Under the cooperative control scenario, CO2 emissions will peak in 2028 and basically achieve the carbon emission target of 2 °C in 2050. SO2, NOX, CO, PM2.5, and NH3 emissions will reach 3.7 Mt., 4.9 Mt., 50.3 Mt., 0.9 Mt., and 2.7 Mt. in 2050, respectively.

Radiative forcing climate footprints in the agricultural sector: Comparison of models from the IPCC 5th and 6th Assessment Reports

To achieve the goal in the Paris Agreement of limiting mean global temperature rise to 1.5 ​°C, total anthropogenic radiative forcing (RF) should be reduced from current 2.7 to around 1.9 ​W ​m−2. A newly developed RF-based climate footprint (RFCF) indicator, which quantifies the additional contribution to RF associated with current and historical emissions, can support transparent alignment with climate stabilization targets by assessing the profile of RF over time. Nevertheless, RFCF applications to date have been based on parameters and equations from IPCC 5th Assessment Report (AR). Considering the latest updates in the IPCC 6th AR, we applied the RFCF approach for the first time in a case study involving the Australian agricultural sector. We compared the RF, RFCF and annual changes in RFCF of CH4, N2O and CO2 using both models. All the results of RF as well as RFCF calculated using the latest model were slightly lower than those obtained using the former model. The agricultural sector's contribution to RF had plateaued in recent years and is projected to reach the point of net zero increase in 2022 (IPCC 6th AR model) or 2023 (IPCC 5th AR model). Considering the latest updates in emission lifetime, radiative efficiency and indirect effects based on the background concentration (1750–2019), the assessments based on IPCC 6th AR model provide more reliable results. However, a dynamic model is required to reflect the additional RF for the pulse emission based on the relevant climate background in the same year. The RF-based footprint approach can support national greenhouse gas emission reduction policy targets, especially for sectors with substantial biogenic methane emissions.

How does public concern about climate change affect carbon emissions? Evidence from large-scale online content and provincial-level data in China

Micro-level concerns about climate change can not only affect individual low-carbon behaviors but also exert intense pressure on carbon emission reduction strategies and policy design. However, whether public concern about climate change can affect per capita carbon emission is still unclear. Based on panel data from 30 provinces in China and 188,943 climate change-related blogs from 2012 to 2019, this research investigates the impact of public concern on per capita carbon emissions. Results show that, in general, public concern about climate change has a significant negative impact on per capita carbon emissions in China, indicating that climate crisis awareness has a supervisory effect on greenhouse gas emissions. Nevertheless, the effects of different public concerns on per capita carbon emissions are different, with the topics of “low-carbon appeal” and “concern for future” have significant positive impacts on per capita carbon emissions, while the topics of “carbon governance performance” and “climate change mitigation strategies” have significant negative impacts on per capita carbon emissions. The findings yield new opportunities to delve into the relationship between the externality of people's will and governmental behaviors and provide in-depth insights for decision-makers to develop tailor-made solutions towards carbon reduction and sustainable development.

A New Method for Reconstructing Tree-Level Aboveground Carbon Stocks of Eucalyptus Based on TLS Point Clouds

Eucalyptus plantation forests in southern China provide not only the economic value of producing timber, but also the ecological value service of absorbing carbon dioxide and releasing oxygen. Based on the theory of spatial colonial modeling, this paper proposes a new method for 3D reconstruction of tree terrestrial LiDAR point clouds for determining the aboveground carbon stock of eucalyptus monocotyledons, which consists of the main steps of tree branch and trunk separation, skeleton extraction and optimization, 3D reconstruction, and carbon stock calculation. The main trunk and branches of the tree point clouds are separated using a layer-by-layer judgment and clustering method, which avoids errors in judgment caused by sagging branches. By optimizing and adjusting the skeleton to remove small redundant branches, the near-parallel branches belonging to the same tree branch are fused. The missing parts of the skeleton point clouds were complemented using the cardinal curve interpolation algorithm, and finally a real 3D structural model was generated based on the complemented and smoothed tree skeleton expansion. The bidirectional Hausdoff distance, average Hausdoff distance, and F distance were used as evaluation indexes, which were reduced by 0.7453 m, 0.0028 m, and 0.0011 m, respectively, and the improved spatial colonization algorithm enhanced the accuracy of the reconstructed tree 3D structural model. To verify the accuracy of our method to determine the carbon stock and its related parameters, we cut down 41 eucalyptus trees and destructively sampled the measurement data as reference values. The R2 of the linear fit between the reconstructed single-tree aboveground carbon stock estimates and the reference values was 0.96 with a CV(RMSE) of 16.23%, the R2 of the linear fit between the trunk volume estimates and the reference values was 0.94 with a CV(RMSE) of 19.00%, and the R2 of the linear fit between the branch volume estimates and the reference values was 0.95 with a CV(RMSE) of 38.84%. In this paper, a new method for reconstructing eucalyptus carbon stocks based on TLS point clouds is proposed, which can provide decision support for forest management and administration, forest carbon sink trading, and emission reduction policy formulation.

Using life cycle assessments to guide reduction in the carbon footprint of single-use lab consumables

Scientific research pushes forward the boundaries of human knowledge, but often at a sizable environmental cost. The reliance of researchers on single-use plastics and disposable consumables has come under increased scrutiny as decarbonisation and environmental sustainability have become a growing priority. However, there has been very little exploration of the contribution of laboratory consumables to ‘greenhouse gas’ (GHG) carbon emissions. Carbon footprint exercises, if capturing consumables at all, typically rely on analyses of inventory spend which broadly aggregate plastic and chemical products, providing inaccurate data and thus limited insight as to how changes to procurement can reduce emissions. This paper documents the first effort to quantify the carbon footprint of common, single-use lab consumables through emission factors derived from life cycle assessments (LCAs). A literature review of LCAs was conducted to develop emission factors for lab consumables, considering the emission hotspots along each product’s life cycle to identify where emission reduction policies can be most effective. Results can be used as inputs for lab practitioners seeking to understand and mitigate their carbon footprint.

Spatiotemporal differentiation and convergence characteristics of green economic efficiency in China: from the perspective of pollution and carbon emission reduction.

Accurate quantification of pollution and carbon emission reduction policies, as well as analysis of green economic efficiency (GEE), are of great significance to accelerating green economic development in China and contributing to pollution prevention and carbon peaking. Using data from 2006 to 2022, this study incorporates pollution and carbon emission reduction policies into the evaluation system, and uses a model with slacks-based measures and a directional distance function (SBM-DDF) to calculate the GEE of 30 provinces. The Dagum Gini coefficient, kernel density estimation, and spatiotemporal convergence analysis are used to analyze the spatiotemporal differentiation and convergence characteristics of GEE. The findings show that the strengths of the pollution and carbon emission reduction policies are increasing but vary greatly among the provinces. China's overall GEE has a time trend with the characteristics of "decline-fluctuation-stable." The Dagum Gini coefficient reveals the relative differences between the major regions. Both the intra-regional and inter-regional differences tend to widen over time and the latter explains most of the sources of the overall differences. Kernel density estimation shows that the absolute differences between the provinces are generally widening, whereas the absolute differences between the provinces in the central and western regions are smaller than those in the eastern region. No obvious σ convergence characteristics exist in the country overall and the three major regions, but β convergence characteristics are present in each region. The factors affecting changes in the GEE of each region are not the same. The study suggests that the China should further improve the implementation of pollution and carbon emission reduction policies, pay attention to the regional differences and convergence issues of GEE, and promote the coordinated development of green economy in different regions. This study innovatively quantifies the policies related to pollution and carbon emission reduction, providing empirical evidence for understanding the performance of pollution and carbon emission reduction policies in various regions. Furthermore, this study incorporates policies as inputs into the GEE evaluation system, reveals the spatiotemporal differentiation of GEE, thereby providing reference for green economic transformation and sustainable development.

Carbon emissions abatement with duopoly generators and eco-conscious consumers: Carbon tax vs carbon allowance

With electrification and economic development, global carbon emissions are rising every year. Among them, the power industry accounted for the highest proportion of carbon emissions. To combat climate change, environmental pollution and economic losses caused by greenhouse effect, governments implement low-carbon policies in the electricity market. Considering the impact of generators’ carbon emissions on consumers behaviors and environmental benefit, this paper compares two main emission reduction policies — carbon tax and carbon allowance in a market with two asymmetric generators and a group of heterogeneous eco-conscious consumers. Despite the widespread belief that environmental advocacy hurts economic performance and social welfare, we find that both policies improve social welfare due to price increases or production restrictions. And when neither generator has a significant cost advantage, the social welfare under carbon allowance is higher than that under carbon tax, and the total carbon emissions are lower. This is because the carbon tax indirectly regulates generators’ output and total welfare by affecting prices. While carbon allowance can directly regulate generators’ output through capacity limits, and further improve the total social welfare. Through numerical analysis, the optimal emission reduction strategies of the government under different conditions are determined. We also extend the main model and show that the key results are still valid.