Average Emission Intensity Research Articles

Is a dedicated bus lane operationally and environmentally beneficial? A case study in Beijing

ABSTRACT In 2023, Beijing adjusted dedicated bus lane (DBL) policies, allowing private car access during specific periods to improve road transport efficiency. This study proposes a comprehensive method to assess the operational and environmental impact of DBL adjustments. Traffic volumes are estimated using a localized traffic fundamental diagram model based on large-scale floating car speed data, while vehicle emissions are quantified employing a high-resolution road network emission inventory. Results show over 20% increased traffic volumes and 10% higher average speed on DBLs in central Beijing. Extended to regional road network, the adjustments enhance expressway capacity and improve traffic efficiency on main and minor arterial roads. While total emissions on expressways increase due to heightened traffic volumes, average vehicular emission intensities in urban areas decrease because of smoother traffic flow. These findings could provide valuable insights for decision-makers with the information needed to target reasonable DBL policies in metropolitan regions.

United States Dairy Farms and Global Warming.

Several metrics have developed for combining the warming effects of various greenhouse gases (GHG). The metric used can affect the life cycle assessment and comparison of dairy production systems due to the weighting placed on long- versus short-lived gases in the atmosphere. Global warming potential with a time horizon of 100 years (GWP-100) has become the standard but metrics are also available for other time horizons. Metrics for 20-, 100- and 500-year horizons gave average farmgate emission intensities of 2.08, 0.98 and 0.50 kg CO2e/kg of fat-and-protein-corrected-milk produced for current US dairy farms. Compared with the use of GWP metrics, which represent energy absorption, use of global temperature-change potential (GTP), combined global temperature-change potential (CGTP) or global warming potential star (GWP*) reduced the warming effect of methane relative to other GHG. These metrics representing temperature change reduced the warming potential of US dairy farms by 17 to 49% compared with the use of GWP-100. The metrics used also affected the comparison of individual production systems providing different life cycle assessments of management practices. Use of GWP-100 metrics indicated that warming from GHG emissions of US dairy farms increased 11-15% between 1971 and 2020, while the use of GTP, CGTP and GWP* metrics showed little or no effect on global temperature change over the 50-year period. Use of GWP-100 metrics indicated that GHG emissions related to milk production on dairy farms represented 1.6% of all US GHG emissions in 2020 while use of other metrics ranged from 0.9 and 1.8%. Although all approaches for representing the integrated warming impact of GHG have benefits and challenges, approaches such as CGTP and GWP* that account for the rate of methane emission relative to the oxidation rate in the atmosphere provide a more process-based assessment of the long-term impact of dairy farms on global temperature and perhaps a more scientifically sound approach for assessing strategies to mitigate the warming effect of dairy farms.

Agricultural Productivity and Climate Mitigation

Agriculture will play a central role in meeting greenhouse gas (GHG) emission targets, as the sector currently contributes ∼22% of global emissions. Because emissions are directly tied to resources employed in farm production, such as land, fertilizer, and ruminant animals, the productivity of input use tends to be inversely related to emissions intensity. We review evidence on how productivity gains in agriculture have contributed to historical changes in emissions, how they affect land use emissions both locally and globally, and how investments in research and development (R&D) affect productivity and therefore emissions. The world average agricultural emissions intensity fell by more than half since 1990, with a strong correlation between a region's agricultural productivity growth and reduction in emissions intensity. Additional investment in agricultural R&D offers an opportunity for cost-effective (<US$30 per ton carbon dioxide) and large-scale emissions reductions. Innovations that target specific commodities or inputs could even further reduce the cost of climate mitigation in agriculture.

Impacts of mode shift on well-to-wheel emissions from inter-capital transport in Australia – Part II: Sea and air transport

Achieving a mode shift in the transport sector will be important in helping Australia to meet its target for net-zero greenhouse gas (GHG) emissions by 2050. However, robust data for Australian transport have previously been unavailable or limited. This paper extends a recent analysis of mode shift impacts for land surface transport (Part I) to include sea transport (freight only) and air transport (passengers and freight) and demonstrates recently developed assessment tools. The analysis considers the potential of inter-mode shifts to reduce well-to-wheel/wake (WTW) emissions (as CO2-equivalents, CO2-e) in 2019, 2030, and 2050, specifically for the transport of either passengers or freight between Brisbane and Melbourne as a case study. The analysis provides emission intensities in grams per passenger-km (g/pkm) and grams per tonne-km (g/tkm), as well as annualized values, and considers the variability and uncertainty in the estimates using a probabilistic approach. For sea freight transport, the average emission intensities are 9 – 16 g CO2-e/tkm for container ships and 4 – 8 g CO2-e/tkm for bulk carriers, depending on the year. For air passenger transport, excluding non-CO2 climate effects, the emission intensity decreases from 166 g CO2-e/pkm in 2019 – 89 g CO2-e/pkm in 2050. Air transport performs particularly poorly for freight; the emission intensity was 1,345 g CO2-e/tkm in 2019, decreasing to 719 g CO2-e/tkm in 2050. The analysis shows that a transfer of passengers from air or road to electric rail, and a transfer of freight from road to sea or electric rail, have the potential to significantly reduce WTW emissions. For example, for passenger transport between Brisbane and Melbourne, the complete transfer of travel from air to electric rail would reduce annual emissions (including non-CO2 effects) by around 95% in both 2030 and 2050. A complete transfer of freight from road to sea would reduce emissions by around 60 – 80%. In 2050, the complete transfer of freight to electric rail from road, diesel rail, and air transport would reduce WTW emissions between the cities by 83%, 81%, and 99.6%, respectively. The study provides valuable new information on mode shift and GHG emissions across all modes. As such, it helps researchers, policy-makers, transport/land-use planners, and network operators to quantify, design, and implement mode shift measures to reduce emissions.

Cellulase-assisted refining in a paperboard mill: Avoided emissions from energy savings. A case study of a Finnish paperboard mill

The 2023 being on its way to be the warmest year in the 174-year global surface temperature history it is becoming more and more evident how crucial it is to break the linkage between economic growth and greenhouse gas emissions.The pulp and paper industry is highly energy intensive industry sector. It is the fourth largest industrial energy user and currently produces approximately 1.3% of global greenhouse gas emissions. While the demand for paper and paperboard products keeps rising, it is essential for the industry to find energy efficient solutions for reducing the environmental impact of the pulp and paper products. 80% of the progress in energy efficiency in Finnish paper mills is due to improved technology and more optimal operational modes. Optimizing existing processes has bigger effect on energy efficiency than closing old and starting new paper mills.Pulp refining is one of the most energy intense steps in the paperboard production, representing 15%–18% of the total energy consumption of a paper mill. The effect of a commercial cellulase enzyme product was evaluated for energy savings and avoided emissions in the refining process of a paperboard mill. Data from a paperboard line using Bleached Softwood Kraft Pulp (BSKP) from a separate pulp mill was provided by a Finnish paperboard mill containing production runs both with and without commercial cellulase product ECOPULP® R. Data was collected from the mill automation system to evaluate and compare a refining capacity restricted run and a run that was not refining capacity restricted.Analysis of the data showed a reduction of refining energy by approximately 16%–17% when the cellulase enzyme was used. This corresponds to 2 kg of CO2 emissions avoided per tonne of pulp refined when calculated using the Finnish average electricity emissions intensity in 2022. Use of the cellulase enzyme enabled the refining results that could not have been reached with the bare mechanical refining capacity of the mill.Cellulases can be efficient processing aids in pulp refining. The reduction of required mechanical refining and electrical power to run the refiners to achieve the required freeness can significantly affect the carbon footprint of the paperboard products, whereas the negative impact of emissions from enzyme production is negligible. Reduction of CO2 emissions is especially significant in the areas where the electricity mix used for the production is based heavily on fossil fuels.

Can converting raw straw into biochar incorporation achieve both higher maize yield and lower greenhouse gas emissions intensity in drought-prone environment?

Converting raw straw into biochar is considered an effective strategy for soil carbon sequestration. However, it remains uncertain whether biochar is more environmentally friendly for achieving higher crop yield in comparison to raw straw in drought-prone regions. Here, a 5-year experiment with three treatments (no incorporation(CK), raw straw incorporation(SI) and straw-derived biochar incorporation(BI)) was conducted to examine the effects of SI and BI on soil physicochemical properties, grain yield (GY), and greenhouse gas (GHG) emissions. Results indicated both SI and BI increased soil water content (SWC), soil temperature (ST), soil porosity, field capacity, pH, total nitrogen and soil organic carbon (SOC) compared with CK. On average, seasonal soil CO2 emission increased significantly by 42.3% in SI and 16.8% in BI, respectively; seasonal N2O emission increased by 26.9% in SI, while decreased by 17.5% in BI. Particularly, the 5-year average GY was as high as 9.4 t ha-1 in BI, significantly higher than that of SI and CK. The 5-year average global warming potential (GWP) increased by 57.9% and 24.6% under SI and BI, respectively, compared with CK. As a result, the average GHG emission intensity (GHGI) increased by 32.0% in SI, whereas it declined by 8.9% in BI. Attribution analysis showed that the CO2 emission primary driven GHGI change under SI, contributing up to 76.4% to GHGI increase. Conversely, the GY increase primary driven GHGI change under BI, contributing to 55.0% GHGI decrease. Therefore, converting raw straw into biochar incorporation acts as an environment-friendly strategy for higher yield harvest.

Wind energy resource assessment and wind turbine selection analysis for sustainable energy production

The objective of this study is to perform an analysis to determine the most suitable type of wind turbine that can be installed at a specific location for electricity generation, using annual measurements of wind characteristics and meteorological parameters. Wind potential analysis has shown that the analyzed location is suitable for the development of a wind farm. The analysis was carried out for six different types of wind turbines, with a power ranging from 1.5 to 3.0 MW and a hub height set at 80 m. Wind power potential was assessed using the Weibull analysis. The values of the scale coefficient c were determined, and a large monthly variation was observed, with values ranging from 1.92 to 8.36 m/s and an annual value of 4.95 m/s. Monthly values for the shape coefficient k varied between 0.86 and 1.53, with an annual value of 1.07. Additionally, the capacity factor of the turbines was determined, ranging from 17.75 to 22.22%. The Vestas turbine, with a nominal power of 2 MW and a capacity factor of 22.22%, proved to be the most efficient wind turbine for the specific conditions of the location. The quantity of greenhouse gas emissions that will be reduced if this type of turbine is implemented was also calculated, considering the average CO2 emission intensity factor (kg CO2/kWh) of the national electricity system.

Modelling of on-farm greenhouse gas emissions from dual-purpose meat and wool sheep production in different geographical regions of Norway

The whole-farm model, HolosNorSheep, was developed to estimate net greenhouse gas emissions from dual purpose sheep production in Norway. The model adopted a cradle-to-farm gate system boundary and is based on the International Panel on Climate Change methodology and includes direct emissions from enteric and manure methane (CH4; GWP100=27), direct and indirect nitrous oxide (N2O; GWP100=273) from manure and soils, direct carbon dioxide (CO2) emissions from energy use and indirect CO2 emissions from the production of input factors. Soil carbon balance is calculated using the ICBM-model. Emission intensities (CO2-eq/kg product) for sheep carcass and greasy wool, was estimated for five geographical regions of Norway (East, West, South, Mid and North). The geographical regions varied considerably in climate, feed resources, days on pasture and herd/farm management. Estimated emission intensities per kg carcass and greasy wool varied from 23.5 to 19.9 (Mid region) to 26.8 and 22.8 kg CO2-eq (North region) respectively. The difference between the Mid and North region was mainly due to higher CO2 and N2O emissions from use of fuel and N-fertilizer in the North, where a short growth season, and thus reduced grass yields, are compensated by using greater land area. Based on the distribution of sheep in the regions, and the emission intensities in each region, weighted average emission intensities per kg carcass and greasy wool were estimated as 25.1 and 23.3 kg CO2-eq, respectively. Five alternative methods for the allocation of the co-products were compared with the amounts allocated to greasy wool ranging from 0 % when using no allocation (i.e., all emissions allocated to the sheep carcass), 11 % for economic allocation, 12 % for mass allocation, 19 % for biophysical allocation based on energy requirement up to 31 % for allocation based on protein mass. The emission intensity for wool was highly sensitive to the allocation method used (range from 0 to 61 kg CO2-eq per kg greasy wool), while the effect on sheep carcass was lower (range 19 to 28 kg CO2-eq per kg carcass). For production systems where meat is the main product, as in Norway, allocation should be based on biophysical allocation based on energy requirement.

Analysis of carbon emissions from social water cycle in the Pearl River Delta of COVID-19: Perspectives from water-energy-carbon nexus

Carbon emissions from various sectors during COVID-19 pandemic have become a hot research topic. However, the impact of COVID-19 on carbon emissions from social water cycle (SWC) processes is unclear. This study uses the Pearl River Delta (PRD) urban agglomeration as the research object and considers the water system carbon emissions in the study region pre- (2018, 2019) and post-COVID-19 (2020, 2021). First, the findings indicate that carbon emissions from SWC systems in PRD cities increased during the COVID-19 pandemic. Guangzhou had the most significant growth of 2.868 Mt, and Zhongshan had the highest annual growth rate of approximately 9%. Second, carbon emission intensity of each subsystem in 2021 is higher than that in other years, with the water use system emitting much more carbon than the other systems. Finally, the amount of water in the SWC showed a significant positive relationship with carbon emissions and average carbon emission intensity. Guangzhou and Shenzhen released more carbon emissions than other cities, and Zhaoqing and Huizhou had a lower average carbon intensity. This study's conclusion differs from other research findings that, indicate a declining carbon emission trend during the COVID-19 pandemic. This study is the first to analyze the changing patterns of carbon emissions from SWC systems during the COVID-19 pandemic. The results can provide carbon recommendations for SWC system development in response to major public health emergencies.

Accounting of aviation carbon emission in developing countries based on flight-level ADS-B data

The aviation industry is one of the key areas to achieve carbon neutrality due to its difficulty in fuel substitution. In developing countries, aviation industry will be in a period of rapid growth for a long time, which means that it will be more affected by the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA). Therefore, aviation carbon emission reduction in developing countries needs to be paid more attention. This study utilizes the full sample data of global developing countries' fight-level information of June 2018, including a total of 1,048,655 flight data, to account for the aviation fuel consumption, CO2 emissions, and emission intensity of 109 developing countries based on 70 aircraft types. The impacts of aircraft structure and flight pattern on energy efficiency and emission intensity are explored by combining micro fight-level aviation data with spatial index decomposition method. The results show that the average emission intensity of developing countries is actually better than that of developed countries. Most of them are concentrated in the range of 65–85 gCO2 per seat-kilometers. The emission reduction potential of traditional measures in developing countries can theoretically close to 20%, of which about three-fifths comes from the adjustment of aircraft structure, while the remainder comes from the improvement in flight pattern. There is still a lot of room for structural adjustment of long-haul large-sized aircraft in developing countries by avoiding less efficient four-engine aircrafts and choosing more efficient ones in terms of new fleet deployments. But the potential for restructuring of medium-sized aircraft is limited, thus needing to further improve efficiency by increasing flight distance. Large-sized aircraft should be avoided as much as possible within the range that medium-sized aircraft can reach, which is crucial in route planning in many developing countries.

Greenhouse gas emission inventory of drinking water treatment plants and case studies in China

The Chinese government claimed to reach carbon dioxide emissions peaking by 2030 and achieve carbon neutralization by 2060. In this context, it's meaningful and urgent to estimate GHG emissions amount in every sectors. The growing concern about reducing GHG emissions has been shared by many water companies. This work aims to identify and estimate GHG emissions from the activities of drinking water treatment plants (DWTPs). According to the GHG protocol, the GHG emission inventory of DWTPs covers the sources of fossil fuel combustion, reservoir emissions, electricity and heat supply, use of chemicals and additives, disposal of waste, transportation, operation and maintenance. The tool was tested by nine DWTPs, which had an average GHG emission intensity of 0.225 kg CO2-eq/m3. The GHG emission intensities range from 0.167 kg CO2-eq/m3 to 0.272 kg CO2-eq/m3. The main source of GHG emissions is electricity supply, followed by the use of chemicals and additives. According to the average emission intensity, the estimated total amount of GHG emissions from DWTPs in China is about 1.82 × 107 t/a, corresponding to 0.15 % of the total GHG emission in China. The proposed GHG sources and emissions help decision-makers and DWTPs companies estimate GHG emissions more accurately and undertake GHG reduction measures.

Modified sectoral average greenhouse gas intensity for more accurate carbon risk analysis

Thanks to the existing data gaps, Monitoring climate-related financial risks is no easy task. Greenhouse gases play an important role in climate change; therefore, examining economic activities’ emissions can be a suitable way to analyse climate risks. However, relying only on activities’ average greenhouse gas emissions can be misleading, as substantial emitters can greatly divert the average values upwards. Modifying these sectoral average emissions by subtracting substantial emitters’ data results in a clearer picture of climate risk analysis. The modified sectoral average method, which primarily relies on data from the emission trading systems, treats companies under the EU Emissions Trading System (ETS) regulation separately. With this treatment, sectors with high emission values can experience a significant drop in sectoral average emission intensity compared to companies under ETS regulation. To use an already available but not applied data cluster is an affordable, easy-to-implement way to increase the accuracy of climate risk indicators. Results in Hungary show that implementing this information reduces sectoral average intensity for companies not under ETS regulation.

Enteric methane emission factors of smallholder dairy farming systems across intensification gradients in the central highlands of Ethiopia

BackgroundFollowing global pledges to reduce greenhouse gas (GHG) emissions by 30% by 2030 compared to the baseline level of 2020, improved quantification of GHG emissions from developing countries has become crucial. However, national GHG inventories in most Sub-Saharan African countries use default (Tier I) emission factors (EFS) generated by the Intergovernmental Panel on Climate Change (IPCC) to estimate enteric CH4 emissions from animal agriculture. The present study provides an improved enteric CH4 emission estimate (Tier II) based on animal energy requirements derived from animal characteristics and performance data collected from about 2500 cattle in 480 households from three smallholder farming systems to represent the common dairy farming in the central highlands of Ethiopia. Using average seasonal feed digestibility data, we estimated daily methane production by class of animal and farming system and subsequently generated improved EF.ResultsOur findings revealed that the estimated average EF and emission intensities (EI) vary significantly across farming systems. The estimated value of EF for adult dairy cows was 73, 69, and 34 kg CH4/cow/year for urban, peri-urban, and rural farming systems, respectively. Rural dairy farming had significantly higher emission intensity (EI) estimated at 1.78 CO2-eq per kg of fat protein-corrected milk (FPCM) than peri-urban and urban 0.71 and 0.64 CO2-eq kg−1 FPCM dairy farming systems, respectively. The EF estimates in this study are lower than the IPCC's (2019) default value for both stall-fed high-productive and dual-purpose low-productive cows.ConclusionsThe current findings can be used as a baseline for the national emission inventory, which can be used to quantify the effects of future interventions, potentially improving the country's commitment to reducing GHG emissions. Similarly, this study suggests that increased animal productivity through improved feed has a considerable mitigation potential for reducing enteric methane emissions in smallholder dairy farming systems in the study area.

Environmental technology import and carbon emissions intensity convergence: Analysis for the Belt and Road Initiative countries

Since the official launch of the Belt and Road Initiative (BRI) in 2013, China and the BRI countries have been working for the implementation of certain environmental measures to make the BRI project green and clean. For this purpose, China and the BRI countries have planned to implement certain environmental measures. Although China can efficiently implement these measures, most of the BRI countries face technological deficiencies and lack of proper environmental plannings. To tackle these deficiencies, the BRI countries can import environmental technology from China. Moreover, they can communicate their environmental protection policies with China for better policy guidance. The current study, therefore, aims to examine whether the BRI countries’ import of environmental technology from China can reduce carbon emissions in these countries. Moreover, it also examines that whether these countries should follow the environmental policy and the import policy of China or they should follow the six European countries (EU-6) with minimum carbon emissions intensity. This study considers a sample of 88 selected BRI countries (BRI-88) for the period 2001–2019. The results obtained with β convergence (based on the panel quantile regression model) suggest that not all the BRI countries but only the BRI countries with relatively higher carbon emissions intensity can significantly reduce their average carbon emissions intensity by importing environmental technology from China. Moreover, BRI countries can follow the environmental policy of China which is more feasible for them. However, regarding the environmental goods import policy, BRI countries can follow both China and the EU-6.

Evaluation and calculation of urban carbon emission reduction potential based on spatiotemporal geographic model

With the growth of urban carbon emissions, found that the current urban carbon emission reduction potential assessment and calculation in carbon emissions data source, research index selection and cost are some problems, to promote the realization of low carbon emission reduction target, it is necessary to use space and space and geographical weighted regression model to obtain the spatial and temporal distribution of urban carbon reduction. This article combined geographic information system (GIS) technology to construct a spatial database. Based on the geographic weighted regression (GWR) model, a time dimension was introduced to construct a spatiotemporal GWR model, which was used to obtain the spatiotemporal distribution information of urban CEs and draw a spatial distribution (SD) map of urban CE reduction potential (RP). This article also conducted experimental tests on the carbon reduction potential (CRP) of Chinese cities by combining different carbon reduction (CR) measures. This paper uses three CR methods to test P of these cities respectively measure A adjust industrial structure, measure B to promote low carbon technology innovation and measure C is to optimize the urban space structure, the experimental results show that the average carbon emission intensity of measure A cities is 0.72 tons / yuan, 0.02 tons / 0,0.04 tons / yuan less than measures B and C; the average CE efficiency of measures A cities is 0.85,0.02 and 0.05 compared with measures B and C respectively. From the above data, adjusting the industrial structure can effectively improve the CE efficiency of various cities, reduce the CE, reduce the CE intensity, and promote the realization of CE reduction in cities.

On the use of time varying marginal and time varying average emissions factors in building energy analysis

Calculation of greenhouse gas emissions from grid consumed electricity requires use of grid emissions intensity factors. In current building emissions regulations annual fixed factors are used, though there is growing recognition of the benefits of time-varying factors that take account of the varying emissions intensity as different generators ramp up/down. There are two main types of time varying emissions factors: marginal (MEF) and average (XEF) emissions intensity. Regulators seeking to draft policy around use of time varying emissions factors will need to evaluate the most appropriate methodology. Several studies in the scientific literature state MEF should be used to calculate savings from flexible demand strategies since it is the marginal generator that is the first to respond to small changes in demand. Here we suggest that XEF should in fact be the preferred approach in most scenarios. This recommendation is based on three factors; (i) alignment with historical emissions accounting methods; (ii) a more consistent basis from which to evaluate the alternative (baseline) scenario; and (iii) greater clarity over the incentive that it provides for investment in renewable generation. These factors are explored using examples and data from the Australian National Electricity Market.

Time-lag effects of flood stimulation on methane emissions in the Dongting Lake floodplain, China

Natural wetlands are the primary sources of CH4 emissions in the natural environment. However, the understanding of CH4 fluxes in floodplain wetlands remains limited. This study employed eddy covariance methods to observe CH4 fluxes over a three-year period in a subtropical wetland floodplain, specifically the Miscanthus sacchariflorus (M. sacchariflorus) ecosystem in Dongting Lake wetland. Our analysis focused on exploring the impact of flooding frequency on CH4 emissions, flood stimulation effect, time-lag effects, and the environmental factors influencing CH4 fluxes. The M. sacchariflorus ecosystem exhibited an annual CH4 emission rate of 14.54 g CH4C m−2 y−1. During the flood period, the average daily CH4 emissions reached 0.155 g CH4C m−2 d−1, contrasting with the pre-flood period's average of 0.014 g CH4C m−2 d−1. Moreover, the time-lag effect of flooding on CH4 emissions was found to be 10 days, representing the period between inundation and a substantial increase in CH4 emissions. Comparatively, in 2021, following three fluctuations in floodwaters, the average CH4 emission intensity during the flood period decreased by 46.2% and 48.9% when compared to the years 2019 and 2020 which both following one fluctuation, respectively. CH4 emissions during flooding are predominantly influenced by water depth (WD), wherein shallow WD corresponds to higher CH4 emissions. This correlation can be attributed to factors such as vegetation type, water-column pressure, and soil oxygen content. Therefore, increasing frequency of inundation and a higher WD hold promise as effective measures for mitigating CH4 emissions in floodplain wetlands.

Emissions associated with the management of household organic waste, from collection to recovery and disposal: A bottom-up approach for Sydney and surrounding areas, Australia

Recent advances in waste management policies and initiatives world-wide have placed greater focus on the potential greenhouse gas emissions-avoidance that can be achieved via sustainable waste management practices. In New South Wales, Australia, household organic waste has been identified as a problematic stream that through better management, could lead to better environmental outcomes and reduced emissions. However, emissions associated with waste management are poorly characterised in Australia, obscuring decision making around optimal pathways that can maximise landfill diversion and minimise emissions. This study addresses this data limitation by estimating and analysing the emissions associated with household organic waste management. The approach applied route optimisation to estimate emissions from points of collection (weekly and fortnightly) to recovery at composting facilities and disposal at landfills, as well as mass balance modelling to estimate direct and indirect emissions associated with windrow composting and mechanical biological treatment. Landfill gas modelling was also used to estimate lifetime emissions from disposal, as well as potential avoided emissions through recovery. Results for the Greater Sydney and surrounding area show total gross greenhouse gas emissions of approximately 390,100 tCO2-e, and balanced by approximately 145,600 tCO2-e of emissions avoidance through landfill diversion. The average net emissions intensity of jurisdictions in the study area was approximately 133 kgCO2-e/t of waste diverted; or 423 kgCO2-e/t of waste managed. Emissions intensity was highest for the mixed waste collection stream, where diversion pathways are limited; and lowest where separate food collection and mechanical biological treatment was employed. Findings indicate that greater separation of food waste from the mixed stream to dedicated food waste collection will result in the greatest improvements in both emissions intensity and landfill diversion.

A hybrid estimation of carbon footprints for urban commuting transportation via path reconstruction

The transportation sector is a major source of carbon emissions, and it is of great significance to study the estimation method of carbon emissions from urban commuting traffic for energy conservation and emission reduction. In view of the difficulty of collecting detailed trip trajectory data, this paper first reconstructs the trip paths via an improved modal choice model and a modified path planning model based on the O-D trip matrix, taking seven single traffic modals and two combined modals into account. In order to estimate the carbon footprints with theoretical accuracy, the bottom-up method is adopted considering the trip modal, vehicle type, power source, vehicle occupancy, operation characteristics and traffic conditions. Meanwhile, faced with the converted carbon emissions from electric vehicles, factors like charging efficiency, vehicular load, regional power structure and transmission loss are further considered in the estimation function. A case study of Changzhou City has been performed to verify the feasibility of the proposed models, where the volume distribution of commuting trips is predicted upon a modified network traffic assignment by TransCAD, and the spatial distribution of carbon emission intensity has further expanded to the adjacent areas via ArcMap analysis tools. The total carbon emission and the average link emission intensity of daily commuting in the study area are about 14.7 × 105 kg/day and 870 kg/km respectively. The discussion results indicate that the CO2 emission of fuel-driven vehicles accounts for over 86%, and the equivalent carbon emission of electric vehicles accounts for about 14% under given modal choices. The correlations of carbon emissions to road levels and zone attributes get further revealed and discussed based on the estimation results.

Can Australia create ‘super’ basins?

The world’s need for sustainable energy requires the upstream industry to utilise and deliver renewables and carbon capture and storage (CCS) at scale. To do that we must focus on where the synergies with low-carbon technologies are greatest. We define these as the energy ‘super basins’ of the future – where large hydrocarbon resources are co-located with plentiful clean electricity and CCS potential. These basins provide a viable pathway for industry to become sustainable. Basins without these attributes are disadvantaged, and face being left behind. This paper will focus on where Australian basins sit on this scale – which are the most advantaged through to disadvantaged? Which have the most viable renewables and CCS/CCUS (carbon capture, utilisation and storage) potential? Advantage also depends on cost – renewable energy must be abundant and affordable. In Australia today, upstream resources are plentiful but advantaged resources are not. But that perspective is not fixed, and there is much industry can do to strengthen its outlook. The average upstream emissions intensity of Australian projects is significantly above the global average – due to remote locations, high energy intensity and contaminants. Using renewables and CCUS to cut emissions is a logical step. Government policy, new technologies and exploration can also make a difference. But what of smaller basins without scale? Can Australian operators create ‘boutique’ basins with niche advantages – such as copious wind or solar power – that enable them to extend their longevity? Making Australia’s upstream sector resilient and sustainable is a huge challenge and will require ambitious, innovative basin-level thinking.