Emission Metrics Research Articles

Exploring alternative fuel solutions: lemon grass oil biodiesel blend with dibutyl ether additive for VCR diesel engines - an experimental analysis

There has been an intense surge in interest in the search for alternative sources of petroleum fuels in the modern world as a result of the inflation of fuel prices and the historic supply gap. When compared to petroleum fuels, biodiesel is becoming an increasingly valuable option due to the fact that it produces less emissions and provides the almost same amount of energy. In point of fact, the prime aim of this work is to explore the possibility of utilizing biodiesel derived from lemongrass oil and including dibutyl ether as an additive for the test diesel engine operating on varied compression ratios. The findings showed that the best operating settings are a 17.5 compression ratio with a blend of 30% biodiesel and 70% diesel fuel. At greater loads, brake thermal efficiency is lower than that of diesel engines. Lower loads result in lower specific fuel usage. Mechanical efficiency at higher loads is highest in the B30 blend, but emission metrics such as CO, CO2, HC, and NOx were reduced with the inclusion of an additive, though HC rose with higher loads of lemongrass oil biodiesel blends. When compared to the B30 biodiesel blend with various composition additives, the B30 + 4% additive has the highest efficiency at the fourth load in terms of both brake power and mechanical efficiency.

Carcinogenic industrial air pollution and postmenopausal breast cancer risk in the National Institutes of Health AARP Diet and Health Study

BackgroundChemicals emitted from industrial facilities include known or suspected mammary carcinogens and endocrine disruptors, but epidemiologic studies are limited. We evaluated associations between air emissions of multiple carcinogenic chemicals and postmenopausal breast cancer risk in a large prospective U.S. cohort. MethodsWe used the U.S. Environmental Protection Agency’s Toxics Release Inventory to estimate historical airborne emissions (1987–1995) of 19 known and probable carcinogens for participants enrolled (1995–1996) in the NIH-AARP Diet and Health Study. Among 170,402 women, 15,124 breast cancers were diagnosed through 2018. We constructed inverse distance- and wind-weighted average emissions metrics within 1, 2, 5, and 10 km of the enrollment address for each chemical. We estimated multivariable adjusted HRs and 95 % CIs for categories (quartiles, tertiles, medians) of each chemical in association with breast cancer overall and separately by type (invasive, ductal carcinoma in situ) and estrogen receptor (ER) status. ResultsWe observed an association between benzene emissions and breast cancer risk that was strongest at 1 km (HRQ4 vs. non-exposed = 2.06, 95 %CI: 1.34–3.17; p-trend = 0.001). The magnitude of the association weakened with increasing distance (2 km HRQ4 vs. non-exposed = 1.17, 95 %CI=0.92–1.49; p-trend = 0.19; 5 km HRQ4 vs. non-exposed = 1.05, 95 %CI=0.94–1.16; p-trend = 0.37; 10 km HRQ4 vs. non-exposed = 0.95, 95 %CI=0.89–1.02; p-trend = 0.19) and appeared to be most relevant for invasive rather than intraductal disease. Overall risk was also elevated for vinyl chloride at 5 km (HR≥median vs. non-exposed = 1.20, 95 %CI=1.01–1.43; p-trend = 0.04), but not 2 km or 10 km. We observed suggestive associations for asbestos, trichloroethylene, and styrene in different subgroup analyses, but risk patterns were not clear across distances. Associations with other chemicals were generally null, with limited evidence of heterogeneity by disease type or ER status. ConclusionsAn increased risk of breast cancer associated with relatively high levels of industrial benzene emissions warrants additional study, particularly among participants with diverse sociodemographic characteristics that live in areas with higher density of industrial facilities.

Rational Exergy Management Model based metrics for minimum carbon dioxide emissions and decarbonization in Glasgow

Addressing climate change is an urgent issue that requires effective and sustainable solutions at multiple scales from the building to the district and city scales. This study focuses on the avoidable carbon dioxide emissions responsibilities due to exergy mismatches between supply and demand at multiple scales based on the Rational Exergy Management Model. Nine key metrics for decarbonization are presented with analyses of three case studies in Glasgow, including a university campus area, and urban emissions scenarios. The existing campus district system involves 42 MW boiler capacity for heat supply and 3.35 MW electric power and heat supply with a combined heat and power system that is responsible for 80.97 kg carbon dioxide per operating hour at design conditions. The system may shift towards carbon neutrality if the combined heat and power system capacity increases to at least 27.3 MW electric power and the boiler is eventually banned. A potential expansion of the district network with solar prosumer buildings indicates that the optimum number of Exergy Stars as a new rating is four out of five when embodiments for solar prosumer buildings are considered. Urban emissions scenarios for Glasgow indicate about 20 MtCO2eq of urban consumption-based emissions in 2020 that are analyzed up to 2030 and 2050 under different decarbonization scenarios. Remaining urban emissions are linked to inefficiencies where exergy mismatches cause emissions responsibilities in the energy system. In comparison to the decarbonization index in the urban emissions scenarios for Glasgow, the increased exergy match at 0.87 is closest to its 2045-year value in the renewable energy based green-growth scenario. The results are useful for guiding other urban areas to embark on an effective approach for mitigation and bring society to a better balance with the planet. Districts and cities can utilize these metrics to upscale climate mitigation and minimize emissions more rapidly.

Does digital-industrial technology integration reduce corporate carbon emissions?

The integration of digital technology and industrial technology emerges as a pivotal avenue for enterprises to attain Sustainable Development Goals (SDGs), promoting the transition towards a sustainable and green trajectory of development. The panel data utilized in this paper encompasses financial, operational, and carbon emission metrics of publicly listed companies in China from 2008 to 2019. This longitudinal dataset enables us to perform robust statistical analyses to evaluate the impacts of Digital-Industrial technology integration on carbon emission intensity over time. The results indicate that: (i) Digital-Industrial technology integration significantly reduces the carbon emission intensity of enterprises, the conclusion remains robust when subjected to endogeneity and sensitivity analyses. (ii) Digital-Industrial technology integration mainly reduces the marginal cost of production and enhances the accumulation of knowledge to reduce the carbon emission intensity of enterprises. (iii) Digital-Industrial Technology Integration manifests more pronounced effects in curbing carbon emissions in regions characterized by robust property rights protection and well-developed digital infrastructure. (iv) Further research finds that Digital-Industrial technology integration increases the green technology innovation and ESG (Environmental, Social, and Governance) performance of enterprises. The results of this study provide empirical evidence for the carbon emission reduction function of the Digital-Industrial technology integration, and also verify the positive externality impact of the Digital-Industrial technology integration on the enterprise green innovation and SDGs.

Effect of carbohydrate type in silages and concentrates on feed intake, enteric methane, and milk yield from dairy cows

Dietary carbohydrate manipulation can be used to reduce enteric CH4 emission, but there is a lack of studies on the interaction of different types of carbohydrates that can affect feed intake and ruminal fermentation. Understanding this interaction is necessary to make the most out of CH4 mitigation feeding strategies using different dietary carbohydrates. The aim of this study was to test the effect on enteric CH4 emission, feed intake and milk production response when cows were fed either grass-clover (GCS) or corn silage (CS) as the sole forage source (55% of dry matter, DM), in combination with either barley (BAR) or dried beet pulp (DBP) as a concentrate (21.5% of DM). Twenty-four (half first and half second parity) cows were used in a crossover design with 2 periods of 21 d each, receiving 2 of 4 diets obtained from a 2 × 2 factorial arrangement of the experimental diet. Feed intake, CH4 emission metrics and milk production were recorded at the end of the experimental periods. The diets had NDF concentrations between 258 and 340 g/kg of DM, and starch concentrations between 340 and 7.45 g/kg of DM (CS-BAR and GCS-DBP, respectively). The effects of silage and concentrate on dry matter intake (DMI) were additive, with the highest feed intake in cows fed COR-BAR, followed by cows fed COR-DBP, GCS-BAR, and GCS-DBP (21.2, 19.9, 19.1, and 18.3 kg/d). Energy corrected milk (ECM) yield was not affected by silage source in first parity cows, but it was higher for cows fed CS than cows fed GCS in second parity. The effects of silage and concentrate on CH4 production (g/d), yield (g/kg of DMI) and intensity (g/kg of ECM) were not additive as cows fed GCS had similar responses regardless of the concentrate used, but cows fed CS had lower CH4 production, yield and intensity, when fed BAR instead of DBP. The lower CH4 production, yield and intensity in cows fed CS-BAR compared with other diets could be partially explained by the nonlinear relationship between ruminal VFA and carbohydrates (NDF and starch) concentration reported in literature, however, we observed a linear relationship between acetate:propionate ratio and CH4 yield, suggesting possible other effects. The effects of silage and concentrate on the ruminal VFA were additive in first parity cows, but not in second parity cows. The interaction between dietary CHO type and parity might indicate an effect of feed intake or the energy balance of the cow. Feeding cows silage and concentrate both rich in starch can result in the lowest enteric CH4 emission.

Enhancing energy and environmental metrics of aqueous ammonia emulsified diesel fuel using carbon-metal oxide nanocomposites: An experimental study

The study explores the impact of a zinc oxide (ZnO) induced carbon nanotube (CNT) nanocomposite on ammonia decomposition in aqueous ammonia (AA) emulsified diesel fuel, while evaluating performance and emissions. Functionalized CNTs induced with ZnO underwent structural and surface morphology analysis. Different concentrations of ammonium hydroxide (5 % and 10 %) were mixed with plain diesel fuel (PDF), and 100 ppm of ZnO/CNT was blended with 10 % AA emulsified fuel. Fuel performance was assessed under various brake power conditions compared to PDF. The results demonstrate that the absorption of latent heat by ammonia and its higher decomposition energy result in a longer ignition delay, thereby increasing peak in-cylinder pressure and net heat release rate. The post-combustion reaction of AA emulsified fuels contributes to increased brake specific fuel consumption (BSFC), brake specific energy consumption (BSEC), hydrocarbon (HC), carbon monoxide (CO), and smoke opacity. However, the dispersion of nanocomposite in AA emulsion enhances the catalytic effect and promotes ammonia decomposition, resulting in combustion characteristics similar to PDF. Notably, there is an observed improvement in BSFC by 8.1 % and BSEC by 10.9 %, respectively. Furthermore, emission metrics such as oxides of nitrogen, HC, CO, and smoke opacity decrease by 4.5 %, 18.8 %, 11.4 %, and 5 %, respectively.

Effects of 3-nitrooxypropanol (Bovaer10) and whole cottonseed on milk production and enteric methane emissions from dairy cows under Swiss management conditions

The objective of this study was to determine the potential effect and interaction of 3-nitrooxypropanol (3-NOP; Bovaer, DSM-Firmenich Nutrition Products Ltd.) and whole cottonseed (WCS) on lactational performance and enteric methane (CH4) emission of dairy cows. A total of 16 multiparous cows, including 8 Holstein Friesian (HF) and 8 Brown Swiss (BS; 224 ± 36 DIM, 26 ± 3.7 kg milk yield, mean ± SD), were used in a split-plot design, where the main plot was the breed of cows. Within each subplot, cows were randomly assigned to a treatment sequence in a replicated 4 × 4 Latin square design with 2 × 2 factorial arrangements of treatments with four 24-d periods. The experimental treatments were as follows: (1) control (basal TMR), (2) 3-NOP (60 mg/kg TMR DM), (3) WCS (5% TMR DM), and (4) 3-NOP + WCS. The treatment diets were balanced for ether extract, crude protein, and NDF contents (4%, 16%, and 43% of TMR DM, respectively). The basal diets were fed twice daily at 0800 and 1800 h. Dry matter intake and milk yield were measured daily, and enteric gas emissions were measured (using the GreenFeed System, C-Lock Inc.) during the last 3 d of each 24-d experimental period when animals were housed in tiestalls. There was no difference in DMI on treatment level, whereas the WCS treatment increased ECM yield and milk fat yield. No interaction of 3-NOP and WCS occurred for any of the enteric gas emission parameters, but 3-NOP decreased CH4 production (g/d), CH4 yield (g/kg DMI), and CH4 intensity (g/kg ECM) by 13%, 14%, and 13%, respectively. Further, an unexpected interaction of breed by 3-NOP was observed for different enteric CH4 emission metrics: HF cows had a greater CH4 mitigation effect compared with BS cows for CH4 production (g/d; 18% vs. 8%), CH4 intensity (g/kg milk yield; 19% vs. 3%), and CH4 intensity (g/kg ECM; 19% vs. 4%). Hydrogen production was increased by 2.85-fold in HF and 1.53-fold in BS cows receiving 3-NOP. Further, a 3-NOP × time interaction occurred for both breeds. In BS cows, 3-NOP tended to reduce CH4 production by 18% at approximately 4 h after morning feeding, but no effect was observed at other time points. In HF cows, the greatest mitigation effect of 3-NOP (29.6%) was observed immediately after morning feeding, and it persisted at around 23% to 26% for 10 h until the second feed provision, and 3 h thereafter, in the evening. In conclusion, supplementing 3-NOP at 60 mg/kg DM to a high-fiber diet resulted in 18% to 19% reduction in enteric CH4 emission in Swiss HF cows. The lower response to 3-NOP by BS cows was unexpected and has not been observed in other studies. These results should be interpreted with caution due to the low number of cows per breed. Finally, supplementing WCS at 5% of DM improved ECM and milk fat yield but did not enhance the CH4 inhibition effect of 3-NOP of dairy cows.

Gas Outburst Warning Method in Driving Faces: Enhanced Methodology through Optuna Optimization, Adaptive Normalization, and Transformer Framework

Addressing common challenges such as limited indicators, poor adaptability, and imprecise modeling in gas pre-warning systems for driving faces, this study proposes a hybrid predictive and pre-warning model grounded in time-series analysis. The aim is to tackle the effects of broad application across diverse mines and insufficient data on warning accuracy. Firstly, we introduce an adaptive normalization (AN) model for standardizing gas sequence data, prioritizing recent information to better capture the time-series characteristics of gas readings. Coupled with the Gated Recurrent Unit (GRU) model, AN demonstrates superior forecasting performance compared to other standardization techniques. Next, Ensemble Empirical Mode Decomposition (EEMD) is used for feature extraction, guiding the selection of the Variational Mode Decomposition (VMD) order. Minimal decomposition errors validate the efficacy of this approach. Furthermore, enhancements to the transformer framework are made to manage non-linearities, overcome gradient vanishing, and effectively analyze long time-series sequences. To boost versatility across different mining scenarios, the Optuna framework facilitates multiparameter optimization, with xgbRegressor employed for accurate error assessment. Predictive outputs are benchmarked against Recurrent Neural Networks (RNN), GRU, Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM), where the hybrid model achieves an R-squared value of 0.980975 and a Mean Absolute Error (MAE) of 0.000149, highlighting its top performance. To cope with data scarcity, bootstrapping is applied to estimate the confidence intervals of the hybrid model. Dimensional analysis aids in creating real-time, relative gas emission metrics, while persistent anomaly detection monitors sudden time-series spikes, enabling unsupervised early alerts for gas bursts. This model demonstrates strong predictive prowess and effective pre-warning capabilities, offering technological reinforcement for advancing intelligent coal mine operations.

Similarity analysis study of the carbon emission metrics through fuzzy entropy clustering

The world has changed to a massive degree in the past thousands of years. Most of the time, the amount of carbon dioxide in the atmosphere remains constant. In the late 18th century, according to the sources of CDIAC and NOOA, the level of carbon dioxide began to rise, and then in the 20th century, it went through the roof, reaching levels that had not been seen in nature for millions of years. The increase in carbon in the atmosphere is the major contributing factor to climate change. The key to reversing the damage is restoring the earth’s delicate, balanced carbon cycle. As carbon cycle depicts the way carbon moves around the earth. It consists of sources that emit the carbon component into the atmosphere. The biological side of the carbon cycle is well balanced due to respiration, where carbon dioxide is released into the atmosphere, then plants, bacteria, and algae take carbon dioxide out of the atmosphere during photosynthesis and the process they use to generate chemical energy. On the other hand, oceans are the best sources and sinks; carbon dioxide is endlessly being absorbed into the ocean and released from the oceans almost exactly at the same rate, which is rapidly influencing the carbon cycle. Similarity is a methodology that has many applications in the real world. The current research article is destined to study how statistics of carbon emission metrics are alike and belong to one cluster. In the current study, the research is destined to derive a similarity analysis of several countries’ carbon emission metrics that are alike and often fall in the range of [0, 1]. And deriving the proximity of the carbon emission metrics leading to similarity or dissimilarity. In the current context of data matrices of numerical data, an Euclidian measure of distance between two data elements will yield a degree of similarity. The current research article is destined to study the similarity analysis of carbon emission metrics through fuzzy entropy clustering.

Optimization of fuel modification parameters for effective and environmentally-friendly energy from plant waste biodiesel

Sustainable energy production from plant waste and non-edible seeds offers a promising pathway towards mitigating climate change, reducing environmental impact, promoting economic development, and enhancing energy security. Although several research works have focused on the conversion of Bauhinia seed waste to biooil as a sustainable energy, none of the studies attempted to improve its overall performance through fuel modification and parameter optimization. This study proposes a novel approach by blending water and 2-Ethylhexyl nitrate (2-EHN) into Bauhinia racemose biodiesel (BRD) to minimize emissions and enhance performance. The biodiesel properties were characterized by Fourier-transform infrared and Gas chromatography-mass spectrometry analysis. The optimum levels of fuel modification parameters, such as BRD, water, and 2-EHN in conventional diesel fuel (CD), were obtained using response surface methodology, branding it as a sustainable and greener energy source for engine applications. Box-Behnken design matrix was chosen to categorize input factors, levels, and output responses. Examination of the biodiesel reveals that carbon-based constituents make a significant contribution, and the physicochemical properties align with ASTM standards. The optimization results indicate that the optimum levels of BRD, water, and 2-EHN in CD are 20.33 %, 10.26 %, and 2.05 %, respectively. The performance and emission metrics closely match the projected values. An in-depth examination of combustion, engine vibration, life-cycle assessment, and techno-economic analysis provides further evidence to reinforce the findings and suggests possible avenues for future research.

Carbon Emission Metrics in South-eastern Europe: Empirical Analysis of Trade and Economic Indicator Effects

Carbon Emission Metrics in South-eastern Europe: Empirical Analysis of Trade and Economic Indicator Effects

Artificial intelligence based-prediction of energy efficiency and tailpipe emissions of soybean methyl ester fuelled CI engine under variable compression ratios

Biodiesel, a widely adopted substitute for conventional mineral diesel, is often derived from raw vegetable oils. The use of non-edible oils for the production of biodiesel has been favored for an extended period of time due to the potential impact on the availability of edible oil as a food source. The generation of soybean methyl ester biodiesel using the transesterification process. The primary objective of this study is to examine the potential use of soybean methyl ester biodiesel (referred to as "S") as a substitute for conventional diesel fuel in a standard light-duty compression ignition four-stroke, single-cylinder, water-cooled engine. The present study aimed to evaluate the impact of increased compression ratio (ranging from 14 to 18) on several combustion, performance, and emissions characteristics. This investigation was conducted using biodiesel blends consisting of 5%, 10%, 20%, and 40% soybean methyl ester, and the results were compared with those obtained using conventional diesel fuel. Additionally, in order to enhance the test results, a training process is being conducted on an artificial intelligence network (ANN). The analysis of the data indicated a positive correlation between the compression ratio (CR) and the in-cylinder pressure values at maximum load conditions. Additionally, a negative relationship was seen between the CR and the brake-specific fuel consumption (BSFC), suggesting a drop in BSFC as the CR rose. Upon evaluating the discharge numbers, it was seen that there was a notable reduction in other pollutants, a fall in NOx emissions, and an elevation in haze levels. Nevertheless, surpassing a sulphur content of 20% has an adverse impact on both emissions and the overall functioning of the engine. Based on the findings of the artificial neural network (ANN), the prediction results demonstrate a commendable capacity to accurately predict the experimental data with the R-value changing from 0.81919 to 0.95028 for all engine performance, combustion, and emission metrics.

A strategic sustainability model for global luxury companies in the management of CO2 emissions

Luxury brands are at the forefront of sustainability efforts and carbon emission reductions to fight climate change. The goal of this paper is to analyze such climate change challenges in terms of cost efforts within large luxury conglomerates. In doing so, financial metrics have been gathered for the top 100 companies in the luxury sector and compared against CO2 emissions metrics with regressive methods. This enables the study of relationships between sustainability and finance to explore if sustainability is expensive and if sustainability is explained by costs, sales, taxes, or investment. Such works allow the setting of conclusions on financial and managerial decisions and, moreover, set a new framework of analysis based on financial variables and the positive or negative impact on CO2 emissions, such as which financial variables generate more CO2 emissions (luxury sales, capital investment and financial cost) and which help to reduce such emissions (cost of goods sold, general expenses and taxes).

Characterization and optimization of waste-derived biodiesel utilizing CNT/MgO nanocomposite and water emulsion for enhanced performance and emission metrics

Efficiently managing agricultural plant residues is a growing concern, leading to a focus on converting waste materials into valuable resources. The Rutaceae family, especially Citrus maxima peel oil (CMPO), has potential for biofuel production, but research on its use remains limited, especially in terms of fuel modification. This study aims to extract biofuel from Citrus maxima peel and characterize its chemical components. To enhance efficiency and align with environmental goals, the study suggests integrating water and a nanocomposite into CMPO. An eco-friendly method was employed to synthesize a carbon nanotube (CNT)-induced magnesium oxide (MgO) nanocomposite that was extensively characterized. Fuel combinations were derived using a Box-Behnken design matrix, and optimization followed, emphasizing performance and emission parameters. Analysis of the elemental composition and free fatty acid profile of extracted CMPO highlights its potential as an alternative fuel. Optimal concentrations of CMPO, water, and nanocomposite in diesel fuel were found to be 27.6%, 12.2%, and 64.9 ppm, respectively. Confirmation tests validated improved performance and emission outcomes under these optimal conditions. Projected performance and emission parameters closely align with experimental findings. The proposed fuel combination is expected to achieve a significant 40% reduction in the demand for petroleum derivatives.

Achieving Sustainability: Energy and Emission Metrics in the European Union, Bulgaria, Hungary, Italy, Poland and Romania’s Fossil Fuel-dependent Transportation

Given the transport sector’s significant contribution to global greenhouse gas emissions and pollution, sustainable transportation is essential in mitigating climate change and environmental degradation. This study examines the final energy consumption and greenhouse gas emissions from fuel combustion in the transport sector of the European Union (EU), Bulgaria, Hungary, Italy, Poland, and Romania. It aims to offer a concrete tool for assessing the impact of transport on emissions by employing a quantitative analysis and ordinary least squares (OLS) models. Only the EU, Hungary and Romania’s results met the assumptions necessary for a valid linear regression model, thus elucidating the complex relationships between the analysed indicators.

Climate Finance Calculator: Data and Methodology

This technical note discusses WRI's Climate Finance Calculator, which is designed to calculate countries’ contributions to international climate finance based on their capacity to pay (economic metrics) and climate responsibility (emissions metrics). It extends WRI’s previous work by providing users with more flexibility to explore the implications of different combinations of countries’ emissions and economic metrics on their climate finance contributions.

Carbon taxation in Singapore's semiconductor sector: a mini-review on GHG emission metrics and reporting

The threat of climate change has catalyzed global endeavors to curb greenhouse gas emissions, with carbon taxation emerging as a pivotal policy instrument. Singapore, akin to Taiwan, has embraced this tool, and its ramifications on their semiconductor industry are both profound and multifaceted. At the outset, the imposition of carbon taxes inevitably escalates production costs for semiconductor firms, compelling them to offset their carbon footprint financially. This escalation, in turn, poses a risk of eroding the industry's competitive edge, nudging firms to contemplate the prospect of migrating to locales with more lenient carbon taxation regimes. However, in juxtaposition to these challenges, carbon taxation unveils a silver lining. It instigates semiconductor entities to recalibrate their operations, infusing energy-efficient technologies and pivoting towards renewable energy avenues. Such transitions not only attenuate their carbon emissions but also curtail their financial burden arising from carbon taxation. This manuscript elucidates a panoramic landscape of both policy innovations and technological strides specific to Singapore's semiconductor arena. It aims to be an instrumental compass for stakeholders, delineating pathways for achieving optimal eco-financial equilibrium in the sector.Graphical

Prediction framework for parking search cruising time and emissions in dense urban areas

AbstractThe growing need for temporary pickups/drop-offs and commercial deliveries is crowding out the already inadequate on-street parking spaces designated for car trips, deteriorating the phenomenon of parking search. This paper: (1) uses empirical data and conducts descriptive and comparative analysis using a spatial lag model to analyze the factors influencing average cruising time (ACT) related to parking search, and (2) proposes a novel framework to predict grid-based ACT and to estimate average emission metrics (AEM). The study inputs an aggregated GPS dataset in a 6-month period to the framework and uses New York City and Los Angeles as case study cities. The descriptive and comparative analysis results support the spatial spillover effect of parking search and reveal that residential area, retail area, accommodation, and food services (hotels, restaurants, bars, etc.) employees are the most significant influencing factors on ACT and that temporary pickups/drop-offs and commercial delivery are also unneglectable sources of parking search. The prediction results show a concentrated distribution of ACT in New York City due to private vehicles’ spillover of parking searches. Los Angeles exhibits a relatively high degree of overlap between parking hotspots and emission blackspots, particularly in areas with intense truck activity, further substantiating the close relationship between truck activities and elevated emissions. Following the key findings, the paper proposes several policy recommendations. In practice, this prediction framework can ingest short-term data to provide ACT prediction maps to identify parking hotspots and emission blackspots.

Stakeholder influences on management control systems for ESG governance and reporting in the global automotive industry

AbstractDrawing on Simons’ levers of control, we develop a theoretical model to explore the influences of stakeholders (customers, suppliers, employees, regulators, communities, industry associations, and investors) on Management Control Systems for achieving strategic environmental, social, and governance (ESG) objectives. We then test the model using data from over 600 firms in the global automotive industry by linking the eleven largest automotive manufacturers with over 500 of their tier‐1 and tier‐2 suppliers and examining whether stakeholder‐relevant metrics impact supplier retention and whether management compensation is tied to ESG governance. We also examine whether stakeholder influences lead to higher ESG scores. We find that stakeholder‐related metrics for emissions, human rights, customer health, employee empowerment and industry associations are related to management compensation systems tied to ESG. We also find significant linkages between stakeholder influences and ESG scores.

The city within the global: A framework for the simultaneous estimation of city emissions metrics

In line with national targets, sub-national governments – including cities – are introducing targets to reduce the emissions associated with economic activity within or associated with a particular geography. Cities are important drivers of not only emissions but also economic activity and are embedded into complex economic systems which reach beyond their boundaries, which can raise major issues in identifying whether a city is assisting in promoting sustainability across a wider spatial level. This paper sets out a methodology to downscale global Input Output tables to city-level and use these to calculate production- (territorial) and consumption-based carbon accounts at the city level simultaneously. Illustrating this for the case of Glasgow, Scotland, we show that the city's territorial emissions are significantly lower than its consumption-based carbon footprint (considering both the Areal and Personal Carbon Footprint), but that both metrics are sensitive to assumptions about the emissions intensity of individual sectors. Our results highlight the importance of data quality and accuracy, and the benefits of local knowledge, rather than the unquestioned use of national metrics.