Emission Projections Research Articles

A Review of Research on the Impact Mechanisms of Green Development in the Transportation Industry

Green development in the transportation industry is a new type of development. As the huge energy consumption and carbon emissions generated by the transportation industry have caused many environmental problems, the healthy and environmentally friendly mode of industrial development has received more and more attention. However, the quantification of green development in the transportation industry varies in terms of boundaries, scope, and methods. Due to digital empowerment, the degree of influence and direction of the factors affecting the green development are not fixed. The prediction of future development prospects is relatively single-minded, lacking a comprehensive simulation scenario setting from multiple perspectives. This paper systematically reviews the research progress of green development of the transportation industry from three aspects: development performance assessment, influence mechanism analysis, and development path exploration. After a critical analysis, this study concludes that (1) a clear methodology is needed to assess the direct and indirect non-desired output results of the transportation industry; (2) considering the endogenization of the level of technology, the influence of the interaction between the influencing factors, etc., on the degree and direction of the role of the factors, a more scientific econometric model should be established for in-depth discussion; (3) resident travel options are an important factor affecting environmental issues in transportation. Carbon emission projections and analyses of emission reduction scenarios should integrate the multiple possibilities of residential preferences and policy incentives. The findings of this paper provide valuable references to the energy saving and emission reduction goals of the transportation industry, and the coordinated development of the industry and the economy.

Industrial carbon emission forecasting considering external factors based on linear and machine learning models

Accurate forecasting of carbon emissions has become a critical task for the government to formulate effective policies and sustainable development. However, previous studies have mainly focused on large-scale carbon emissions forecasting, while urban-level carbon emission forecasting is equally important but rarely covered. In this study, we propose a novel carbon emission forecasting framework combining linear and machine learning models that considers both time dynamics and external influences. To improve the accuracy and explanatory power of the proposed model, we first introduce twelve initial influencing factors by considering the urban development, economic development, industrial energy consumption, and demographic factors. And then Lasso regression algorithm is adopted to filter out the indicators with poor predictive power. Finally, a combined prediction model by integrating Autoregressive Integrated Moving Average (ARIMA) and Support Vector Regression (SVR) models is established to capture linear and nonlinear features, respectively. The simulation results show that compared with benchmark models, the proposed model indicates stronger prediction performance with a Mean Absolute Percentage Error (MAPE) of 0.096 and a R-squared (R2) of 97.5%. In addition, six future development scenarios, including carbon emission projections for industrial growth and environmental protection factors, are also performed in this study to provide recommendations for carbon emission reduction programmers and related policy formulation. In conclusion, the forecasting framework proposed in this research can help to identify the key factors affecting carbon dioxide emissions and provide a quantitative reference for carbon dioxide emission reduction.

Ship emission projections based on time series forecasting model for sustainable shipping in the strait of Malacca and Singapore

As maritime activities continue to play a pivotal role in global trade, concerns over ship emissions’ environmental impact have intensified. This study presents detailed projection of ship emissions in Strait of Malacca and Singapore (SOMS), based on Automatic Identification System (AIS) data. By harnessing the rich AIS dataset, emission profiles were developed out of the ship activities data. To envision a sustainable maritime future, we incorporate some possible scenarios around the region combined with a time series forecasting model to project the future conditions of ship emission in SOMS. By analysing the conditions in each scenario, essentials for shaping intelligent systems for efficient maritime traffic can be discovered. Our analysis considers evolving factors such as various ship properties, operational modes, and trajectories. The results provide insights for policymakers, industry stakeholders, and environmental planners seeking to mitigate the local maritime sector’s carbon footprint. This study signified the value of AIS data-driven approach to facilitate regional strategist in confronting resolutions for greener maritime operation, aligning with the transition to intelligent and sustainable practices in the maritime industry within the SOMS.

A Novel High‐Resolution In Situ Tool for Studying Carbon Biogeochemical Processes in Aquatic Systems: The Lake Aiguebelette Case Study

AbstractLakes and reservoirs are a significant source of atmospheric methane (CH4), with emissions comparable to the largest global CH4 emitters. Understanding the processes leading to such significant emissions from aquatic systems is therefore of primary importance for producing accurate projections of emissions in a changing climate. In this work, we present the first deployment of a novel membrane inlet laser spectrometer (MILS) for fast simultaneous detection of dissolved CH4, ethane (C2H6) and the stable carbon isotope of methane (δ13CH4). During a 1‐day field campaign, we performed 2D mapping of surface water of Lake Aiguebelette (France). Average dissolved CH4 concentrations and δ13CH4 were 391.9 ± 156.3 nmol L−1 and −67.3 ± 3.4‰ in the littoral area and 169.8 ± 26.6 nmol L−1 and −61.5 ± 3.6‰ in the pelagic area. The dissolved CH4 concentration in the pelagic zone was 50 times larger than the concentration expected at equilibrium with the atmosphere, confirming an oversaturation of dissolved CH4 in surface waters over shallow and deep areas. The results suggest the presence of CH4 sources less enriched in 13C in the littoral zone (presumably the littoral sediments). The CH4 pool became more enriched in 13C with distance from shore, suggesting that oxidation prevailed over epilimnetic CH4 production and it was further confirmed by an isotopic mass balance technique with the high‐resolution data. This new in situ fast response sensor allows one to obtain unique high‐resolution and high‐spatial coverage data sets within a limited amount of survey time. This tool will be useful in the future for studying processes governing CH4 dynamics in aquatic systems.

Modelling above-ground biomass estimation using combination of vegetation height and forest canopy density at secondary peat swamp forest of Kalimantan

Rapid estimation of carbon stock, particularly carbon from Above-Ground Biomass (AGB) in a high-potential carbon forest, is required to assess potential carbon emission projects. Satellite imagery has been widely used as the main data source to rapidly estimate AGB. Canopy Height (CH) data from NASA’s Global Ecosystem Dynamics Investigation (GEDI) and Forest Canopy Density (FCD) data from satellite Landsat 8 imagery are widely used to estimate AGB. An allometric model with multiple linear regression equations has been used to estimate the AGB value using both parameters of the FCD value and the CH value. 43 values of calculated AGB by non-destructive field survey located in a peat swamp forest area use as a training dataset to develop an allometric model. The CH and FCD indices performed several tests, and the test result met the requirements for developing allometric equations. The coefficient correlation (r) of CH and AGB from 43 sample plots is 0.546, indicating a moderate correlation. The coefficient correlation (r) for FCD and AGB is 0.267 which indicates a weak correlation. The validation test was carried out to assess AGB from the developed allometric model and AGB of field survey with a non-destructive method. The validation result of the allometric model has the value of coefficient correlation (r), RMSE, and R2, respectively 0.569; 22.235; and 0.324. In terms of allometric model validation, another forest with typical conditions, particularly basal area, the model’s AGB result, and the validated area have RMSE 46.506 and r value 0.664.

Carbon Emission Composition and Carbon Reduction Potential of Coastal Villages under Low-Carbon Background

Rural buildings have high optimization potential as a major source of carbon emissions. However, the current research on carbon reduction in rural buildings is rough and lacks categorization and geographic studies. Coastal villages are more economically developed than other types of villages and have greater potential for energy saving. Therefore, this study takes the carbon emission data of buildings and life in 409 villages in typical coastal provinces of China as the basis and proposes optimization strategies for carbon reduction in coastal village buildings via cluster analysis and correlation analysis. The results show that the carbon emission characteristics of coastal villages can be categorized into three scenarios: for scenario 1, villages, their population, and village cultivated area are the core influencing factors of carbon emission, while for scenarios 2 and 3, the most central influencing factors are coal and electricity consumption. Therefore, different types of villages should be guided differently when studying carbon sinks and carbon emission projections in coastal villages. This study aims to establish a low-carbon performance quality assessment and optimization pathway for coastal villages, and the analysis of carbon emission influencing factors and the assessment and optimization provide theoretical support and quantitative methods for the optimization of carbon reduction in villages.

Potential and critical pathways of CO2 emissions reduction: A city-based study

China has put forward the "Dual Carbon" target, carbon peak by 2030 and carbon neutrality by 2060. It is necessary to study the current situation of carbon emissions, carbon reduction potential, and decarbonization paths of industrial cities in China. Taking Wuhan, a typical industrial city, as an object, anthropogenic carbon emissions and distribution in Wuhan in the last 20 years are analyzed. The extended STIRPAT and the gray relation models are adopted to construct a decomposition model for discerning the driving factors of carbon emissions in Wuhan. Besides, a scenario-driven carbon emission projection system with five development scenarios is established, where carbon sinks from the ecosystem and CCUS technology and the decoupling status of “CO2 emissions-GDP” are considered, to assess Wuhan's carbon emissions reduction potential and decarbonization pathways by 2060. The results show that Wuhan's carbon emissions mainly come from the industrial and transportation sectors and that the increase in the share of tertiary industry and the decrease in carbon intensity are the main factors curbing Wuhan's carbon emissions. To realize the “Dual Carbon” target, Wuhan's decarbonization development should start with the ISI (Industrial Structure improving) scenario and focus mainly on the development of tertiary industry between 2022 and 2035. Between 2035 and 2045, the decarbonization development transitions to the BID (Comprehensively improving) scenario, which requires simultaneous accelerating improvement of the city's economy and energy structure. Finally, carbon neutrality can be achieved between 2050 and 2060 through ecosystem carbon sinks and technology carbon sinks.

City-level population projection for China under different pathways from 2010 to 2100

Cities play a fundamental role in policy decision-making processes, necessitating the availability of city-level population projections to better understand future population dynamics and facilitate research across various domains, including urban planning, shrinking cities, GHG emission projections, GDP projections, disaster risk mitigation, and public health risk assessment. However, the current absence of city-level population projections for China is a significant gap in knowledge. Moreover, aggregating grid-level projections to the city level introduces substantial errors of approximately 30%, leading to discrepancies with actual population trends. The unique circumstances of China, characterized by comprehensive poverty reduction, compulsory education policies, and carbon neutrality goals, render scenarios like SSP4(Shared Socioeconomic Pathways) and SSP5 less applicable. To address the aforementioned limitations, this study made three key enhancements, which significantly refines and augments our previous investigation. Firstly, we refined the model, incorporating granular demographic data at the city level. Secondly, we redesigned the migration module to consider both regional and city-level population attractiveness. Lastly, we explored diverse fertility and migration scenarios.

Strategic and Planning Documents as Response to Adaptation to Climate Change

Although the impact of climate change cannot be certainty predicted, research shows that extreme weather events, such as droughts or heavy rainfall and floods, have become significantly more frequent in recent decades. Therefore, climate change is increasingly a risk and can significantly affect human health, ecosystems, the local and global economy, as well as social, political and cultural patterns. In response to the mentioned challenges that climate change brings, activities related to adaptation appear in the world. One of the first steps is the adoption of national strategic and planning documents that will establish guidelines and a roadmap to a climate-resilient society. In the first part of the paper, we dealt with determining the current strategic and legislative direction of the development of the system of adaptation to climate change of the Republic of Serbia and the surrounding countries ‒ Republic of Srpska, Montenegro, North Macedonia and Croatia. Mutual similarities and differences in the strategic and legislative frameworks of the mentioned countries were determined, while in the second part of the paper, the AAA model was used to examine the prevalence of climate change in the adopted national planning documents. The results of the paper indicate a relatively good (in the Spatial Plan of the Republic of Serbia and the Spatial Development Strategy of the Republic of Croatia) to insufficiently (in the Spatial Plan of the Republic of Srpska, Montenegro and North Macedonia) represented problems of climate change in the analyzed planning documents. The least represented category is Analysis, which is, among other things, the result of an underdeveloped system of monitoring and projection of future greenhouse gas emissions, as well as assessing the sensitivity of certain systems (traffic, sanitary, water) to climate change. The Action component is the most represented in all analyzed plans. The reason is that the planning documents defined development guidelines that are indirectly related to climate change (revitalization and reuse of brownfield sites, renewable sources and solar energy, energy efficiency, management, control and treatment of wastewater, development of multimodal transport systems) but as such, an integral part of climate change adaptation policies, and therefore significant.

The land sector in the low carbon emission strategies in the European Union: role and future expectations

ABSTRACT The Land-Use, Land-Use Change and Forestry sector (LULUCF) role is of critical importance in contributing to the ambitious targets set by the European Union (EU) to reduce by 55% net greenhouse gas (GHG) emissions by 2030, compared to 1990 levels, and to become carbon neutral by 2050. The EU LULUCF regulation, approved in 2023, sets out binding targets for each individual Member State to be achieved by 2030, totaling 310 MtCO2e of net removals for the whole EU. However, it remains poorly understood to what extent the EU LULUCF climate target matches with the Member States’ strategies. The alignment between the EU governance and its Member States’ visions for the long-term will determine the achievement of the climate targets. The objective of this study is to understand the LULUCF expected contribution to the EU’s 2030 and 2050 climate goals. In doing so, we explored the European and country-level visions of LULUCF with respect to climate change mitigation and adaptation, as expressed in their Long-Term Strategies (LTSs) and national projections; we evaluated whether national level projections for LULUCF are aligned with the EU short and long term targets. We found that most countries’ LTSs envisage policies and measures in the LULUCF sector, however they are, often general and not comprehensive. Furthermore, the majority of countries’ quantitative future projections of GHG emissions and removals from LULUCF differ from the pathway set in EU targets; thus, countries may need to either update existing policies or conceive and plan new policies and actions.

Plastic protective nets: A significant but neglected “reservoir” for priority chemicals as revealed by composition analysis

As chemical-intensive products, plastics are potential sources of emerging contaminants and pose risks to the ecosystem. However, knowledge on the inventory and emissions of chemicals in plastics remains scarce, prohibiting the lifecycle assessment of their environmental exposure. Herein, full compositions of plastic protective nets (PPNs, one globally used plastics) were analyzed via nontarget screening with mass spectrometry, optical emission spectrometry, infrared spectroscopy and thermogravimetric analysis. Nontarget screening identified 861 non-polymeric organic chemicals, which were classified by network-like similarity analysis into 9 communities, dominated by phthalates (PAEs), aliphatic/oxalic esters and branched alkanes. Notably, around 80.8% (696) of the chemicals were first observed in plastics, suggesting aplenty plastic additives have previously been overlooked. Quantification results indicated PPNs contained higher levels of priority chemicals, including detrimental lead (1.17 × 104 ng/g), benzotriazoles ultraviolet stabilizers (6.66 × 103 ng/g) and PAEs (1.87 × 104 ng/g) than other plastics commonly reported. Emission projections revealed that dibutyl phthalate in PPNs had an annual release (1.83 × 103 kg) comparable to that from greenhouse films in China. These findings suggest PPNs are a significant but neglected “reservoir” for priority chemicals, which could inform future research on resolving plastic compositions, so as to promote sound chemical management.

Bottom-Up Projection of Regional Carbon Emissions from Passenger Road Vehicles Based on Discrete-Time Markov Chain

Accounting for a significant portion of global carbon emissions, the road transportation sector is unequivocally one of the largest emitter among various sectors and a wide variety of mitigation measures has been developed and adopted. The estimation of carbon emissions is a prerequisite for assessing the effectiveness of individual measures. This study outlines the estimation of carbon emissions from road transport based on a discrete-time Markov chain employed to describe the population of road vehicles by fuel type over time. The procedure outlined in the IPCC Guidelines for National Greenhouse Gas Inventories is adopted to estimate the carbon emissions based on the country-specific parameters, which is then applied to the projection of regional carbon emissions from road transport in the Metropolitan Busan City, Republic of Korea. More specifically, country-specific parameters are derived from publicly available data related to carbon emissions from road transport. It is asserted that publicly available data can be effectively collated and incorporated to develop the discrete-time Markov chain to estimate the population of road vehicles, based on which carbon emissions from road transport can then be estimated.

The Importance of Dynamic Iron Deposition in Projecting Climate Change Impacts on Pacific Ocean Biogeochemistry

AbstractDeposition of mineral dust plays an important role in upper‐ocean biogeochemical processes, particularly by delivering iron to iron‐limited regions. Here we examine the impact of dynamically changing iron deposition on tropical Pacific Ocean biogeochemistry in fully coupled earth system model projections under several emissions scenarios. Projected end‐of‐21st‐century increases in central tropical Pacific dust and iron deposition strengthen with increasing emissions/radiative forcing, and are aligned with projected soil moisture decreases in adjacent land areas and precipitation increases over the equatorial Pacific. Increased delivery of soluble iron results in a reduction in, and eastward contraction of, equatorial Pacific phytoplankton iron limitation and shifts primary production and particulate organic carbon flux projections relative to a high emissions projection (SSP5‐8.5) wherein soluble iron deposition is prescribed as a static climatology. These results highlight modeling advances in representing coupled land‐air‐sea interactions to project basin‐scale patterns of ocean biogeochemical change.

Carbon Emission Projection and Carbon Quota Allocation in the Beijing–Tianjin–Hebei Region of China under Carbon Neutrality Vision

Supported by the coordinated development strategy, the Beijing–Tianjin–Hebei (BTH) region has achieved rapid development but also faces severe energy consumption and environmental pollution problems. As the main responsibility of emission reduction, the coordinated and orderly implementation of carbon emission reduction in Beijing, Tianjin, and Hebei is of great significance to the realization of the carbon neutrality target. Based on this, this study comprehensively uses the expanded STIRPAT model, optimized extreme learning machine (ELM) network, entropy method, and zero-sum gains DEA (ZSG-DEA) model to explore the carbon emission drivers, long-term emission reduction pathway, and carbon quota allocation in the BTH region. The results of the driving factor analysis indicate that the proportion of non-fossil energy consumption is a significant driving factor for Beijing’s carbon emissions, and the improvement of the electrification level can inhibit the carbon emissions. The total energy consumption has the greatest impact on the carbon emissions of Tianjin and Hebei. The simulation results reveal that under the constraint of the carbon neutrality target, Beijing, Tianjin, and Hebei should formulate more stringent emission reduction measures to ensure that the overall carbon emission will reach its peak in 2030. The cumulative emission reduction rate should exceed 60% in 2060, and negative carbon technology should be used to offset carbon emissions of not less than 360 million tons (Mt) per year by 2060. Furthermore, the allocation results show that Beijing will receive a greater carbon quota than Hebei. The final allocation scheme will greatly promote and encourage carbon emission reduction in Hebei Province, which is conducive to achieving the goal of carbon neutrality.

Impact of Legislated and Best Available Emission Control Measures on UK Particulate Matter Pollution, Premature Mortality, and Nitrogen-Sensitive Habitats.

Past emission controls in the UK have substantially reduced precursor emissions of health-hazardous fine particles (PM2.5) and nitrogen pollution detrimental to ecosystems. Still, 79% of the UK exceeds the World Health Organization (WHO) guideline for annual mean PM2.5 of 5μgm-3 and there is no enforcement of controls on agricultural sources of ammonia (NH3). NH3 is a phytotoxin and an increasingly large contributor to PM2.5 and nitrogen deposited to sensitive habitats. Here we use emissions projections, the GEOS-Chem model, high-resolution data sets, and contemporary exposure-risk relationships to assess potential human and ecosystem health co-benefits in 2030 relative to the present day of adopting legislated or best available emission control measures. We estimate that present-day annual adult premature mortality attributable to exposure to PM2.5 is 48,625 (95% confidence interval: 45,188-52,595), that harmful amounts of reactive nitrogen deposit to almost all (95%) sensitive habitat areas, and that 75% of ambient NH3 exceeds levels safe for bryophytes and lichens. Legal measures decrease the extent of the UK above the WHO guideline to 58% and avoid 6,800 premature deaths by 2030. This improves with best available measures to 36% of the UK and 13,300 avoided deaths. Both legal and best available measures are insufficient at reducing the extent of damage of nitrogen pollution to sensitive habitats. Far more ambitious reductions in nitrogen emissions (>80%) than is achievable with best available measures (34%) are required to halve the amount of excess nitrogen deposited to sensitive habitats.

Carbon mitigation and health effects of fleet electrification in China’s Yangtze River Delta

Fleet electrification is one of the most promising strategies to mitigate carbon emissions and improve air quality. This study provides a comprehensive analysis of the currently unclear CO2 mitigation and human health benefits from electric vehicle (EV) adoption and energy decarbonization in the Yangtze River Delta (YRD) region by integrating fleet modeling, emission projection, air quality modeling and health risk assessment. Based on future socioeconomic trajectories, we project that the total vehicle stock in the YRD region will peak at 107–117 million around 2045–2050. The transition to EVs combined with largely renewable energy in the YRD region can potentially reduce CO2 emissions by 870 Tg in 2060 and brings along substantial health co-benefits with ∼360 avoided premature deaths per million from reduced PM2.5 and O3 concentrations. This study further explores the NO2-attributable burden from road transportation and reveals that fleet electrification could yield greater NO2-attributable health benefits than those from reduced PM2.5 and O3, especially in traffic-dense urban areas. Those findings indicate that China’s near-term energy development plans (35% renewable energy) have created the conditions for large-scale EV adoption. Our results imply that the benefits of EVs exhibit substantial spatial heterogeneity, underscoring the importance of region-specific EV incentive policies, and hint that policymakers should prioritize densely populated megacities to maximize the potential for public health gains.

The Social Cost of Ozone-Related Mortality Impacts From Methane Emissions.

Atmospheric methane directly affects surface temperatures and indirectly affects ozone, impacting human welfare, the economy, and environment. The social cost of methane (SC-CH4) metric estimates the costs associated with an additional marginal metric ton of emissions. Current SC-CH4 estimates do not consider the indirect impacts associated with ozone production from changes in methane. We use global model simulations and a new BenMAP webtool to estimate respiratory-related deaths associated with increases in ozone from a pulse of methane emissions in 2020. By using an approach consistent with the current SC-CH4 framework, we monetize and discount annual damages back to present day values. We estimate that the methane-ozone mechanism is attributable to 760 (95% CI: 330-1200) respiratory-related deaths per million metric tons of methane globally, for a global net present damage of $1800/mT (95% CI: $760-$2800/Mt CH4; 2% Ramsey discount rate); this would double the current SC-CH4 if included. These physical impacts are consistent with recent studies, but comparing direct costs is challenging. Economic damages are sensitive to uncertainties in the exposure and health risks associated with tropospheric ozone, assumptions about future projections of NOx emissions, socioeconomic conditions, and mortality rates, monetization parameters, and other factors. Our estimates are highly sensitive to uncertainties in ozone health risks. We also develop a reduced form model to test sensitivities to other parameters. The reduced form tool runs with a user-supplied emissions pulse, as well as socioeconomic and precursor projections, enabling future integration of the methane-ozone mechanism into the SC-CH4 modeling framework.

COMPARATIVE STUDY ON URBAN TRANSPORT AND THE ENVIRONMENT (CUTE) FRAMEWORK AS STRATEGY FOR THE REDUCTION OF CARBON DIOXIDE EMISSIONS OF ROAD VEHICLES AT ZAPOTE STREET

Carbon emissions from vehicles contribute significantly to carbon footprint production, which is one of the primary causes of climate change. In Caloocan City, increasing air pollution is observed despite the drastic decrease in air pollutants in major Metro Manila cities. This study primarily aims to provide strategies to reduce carbon dioxide emissions in Zapote St., Caloocan City, through a Comparative study on Urban Transport and the Environment (CUTE) Framework. The study identified that the hourly average carbon footprint contributed by private and public vehicles along Zapote Street is 686.27 and 243.71 kg CO2e on weekdays and 634.65 and 212.97 kg CO2e on weekends. The exploratory data analysis via Python shows that only the vehicle and fuel types affect the emissions volume. Accordingly, the Unified Vehicular Volume Reduction Program (UVVRP) was ineffective. It only resulted in additional private vehicles and increased congestion, indicating the need for an improved public transport system to encourage Filipinos to shift to public transport. Furthermore, the Traffic Volume Emission Projection (TVEP) model projects a decrease of 87.19 kg CO2e per hour in 2023 and 164.72 kg CO2e per hour by 2052, resulting from the 20% reduction in volume upon the implementation of the "no exemption of motorcycles" in the number coding scheme of Caloocan city.

Low Transportation Emission Analysis and Projection Using LEAP: The Case of Qatar

The transportation sector is a significant source of pollution and greenhouse gas (GHG) emissions contributing to global warming. Although research on the actual emissions from transport has been conducted in several parts of the world, very limited outcomes in this field have been reported in the Gulf region. This is especially true for road transportation, one of Qatar’s most significant sources of air pollution. To address this research gap, this study provides an invaluable resource for policymakers as it is the first to quantify the current and forecast future transport emissions. In this work, actual data on traffic counts were input into the Low-Emissions Analysis Platform (LEAP) tool, which has never been used in the context of Qatar, making it an outstanding addition to the research in this domain. The ultimate goal is to estimate the transportation emissions from road traffic in Qatar and investigate how different actions lead to three different scenarios that can impact it. Considering that the policy reformation in the transportation sector is crucial to reducing greenhouse gas emissions and climate change, a scenario analysis can provide theoretical support for policy development and implementation. The investigation used the actual vehicle count data from 2017 to 2021, while all future projections considered the duration from 2022 to 2050 using three scenarios. The first was based on the historical growth in the number of vehicles; the second was based on the business-as-usual scenario (BAU); and the third considered that the public transport shall be divided into three sub-scenarios, namely fuel-economy improvement, the electrification of public transportation, and the replacement of diesel or petrol fuel with compressed natural gas (CNG). The results show that carbon dioxide (CO2) emissions were the highest in all scenarios. Moreover, it was found that in the BAU, the light-duty vehicles (LDVs) category is the most significant contributor to GHG emissions compared to motorcycles and heavy-duty vehicles (HDVs). In addition, it was found that emissions can be reduced by improving public transportation, switching to a cleaner fuel, and reducing reliance on private vehicles.

Characterizing VOCs emissions of five packaging and printing enterprises in China and the emission reduction potential of this industry

The packaging and printing industry has become an important source of industrial volatile organic compounds (VOCs) emissions in China, due to the abundant consumption of organic materials. Effective control measures must be developed which rely on a comprehensive understanding of both current emission characteristics and future emission projections. However, representative VOCs source profiles and updated EFs of packaging and printing industry in China are still lacking. In order to fully explore the VOCs emissions characteristics of packaging and printing industry, field measurements including 17 processes in five packaging and printing factories were conducted in this study. The emission factors (EFs), VOCs source profiles, ozone formation potential (OFP) and secondary organic aerosols potential (SOAP) of 110 VOCs species were acquired. Speciated VOCs emission inventory of packaging and printing industry was compiled based on the activity data in 2020. The future reduction perspectives of packaging and printing industry in China were projected. The VOCs EFs of different materials were estimated, including solvent-based ink (287 g/kg), water-based ink (117 g/kg), soybean oil ink (44 g/kg), UV curing ink (22 g/kg), solvent-based adhesive (420 g/kg), and water-based adhesive (139 g/kg). The VOCs EFs of products were 35.58 kg/t for plastic wrapper, 7.28 g/m2 for soft package plastic, 0.083 g/piece for carton, 0.54 g/piece for tin case, and 0.14 g/piece for aluminum can. The total VOCs emissions in Chinese packaging and printing industry was estimated to 634.32 Gg in 2020. Guangdong, Zhejiang, Jiangsu, Fujian, Shandong, and Sichuan contributed 73% to total VOCs emissions. Printing workshop was the main contributor to both primary emission and secondary pollutant generation. OVOCs was the most abundant specie in three sub-industries. The key VOCs species in PF were ethylacetate, ethanol, 2-pentanone, methyl ethyl ketone, and i-propanol. I-propanol, ethanol, m-tolualdehyde, and dichloromethane were dominated in PP. Ethanol, m/p-xylene, i-propanol, o-xylene, and acetaldehyde were the key species in MC. The key VOCs species to OFP and SOAP were ethanol, methyl ethyl ketone, 2-pentanone, ethyl methacrylate, acetaldehyde, toluene, and xylene, which should be prioritized. The VOCs emissions would be reduced by 46.5% in 2025 and 66.4% in 2030 with stringent control measures. Increasing the usage proportion of low-VOCs-contained raw material and improving treatment efficiency are the key technologies to reduce the VOCs emissions in the packaging and printing industry.