Low Emission Research Articles

The Future of Agriculture in A Carbon Constrained World

In a world grappling with the far-reaching consequences of climate change and a growing imperative to lower emissions of greenhouse gases, agriculture’s potential finds itself at a critical juncture. This paper explores the intricate dynamics of agriculture in a carbon-constrained world, presenting a nuanced analysis of the multifaceted challenges and opportunities that lie ahead. With a focus on sustainable practices, carbon sequestration, and innovative technologies, this study elucidates the pathways through which agriculture can transition towards a low-carbon and climate-resilient future. From precision farming and agroforestry to advanced breeding techniques and digital agriculture, a spectrum of strategies emerges to mitigate the carbon footprint of agriculture while concurrently enhancing productivity and food security. Moreover, the paper underscores the indispensable role of policy frameworks, international cooperation, and public-private partnerships in ushering in a transformative era for agriculture that aligns harmoniously with global climate goals. This review explores carbon foot printing methodologies in agriculture and their implications for sustainable practices. Additionally, it highlights role of carbon foot printing in informing decision-makers, examines emerging trends like precision agriculture, and addresses associated challenges. This review identifies these gaps and offers recommendations for future research. In elucidating these key themes, this paper advances to a thorough comprehensive understanding of the imperative to embrace innovation and sustainability as guiding principles for the future of farming in a carbon-constrained world.

Distinguishing direct N2O emissions from 15N-labeled manure compost, urea, and maize roots in a field study

Nitrous oxide (N2O) emissions from agricultural activities are a significant contributor to global anthropogenic N2O emissions. Sustainable agriculture practices, including using manure compost to replace nitrogen (N) fertilizers, can reduce chemical fertilizer use and minimize negative impacts on soil health and water quality. However, the direct contributions of manure compost to N2O emissions have not been fully studied. We conducted a field study to distinguish the direct contributions of manure compost, urea, and residual maize roots to N2O emissions in an integrated crop-livestock agricultural system in Shandong Province, China. Using the 15N-labeling technique, we found that the majority (93.3%) of cumulative N2O emissions originated from the soil and other unaccounted sources. Notably, manure compost contributed 1.3%, while current-year urea, residual urea with root exudates, and maize root accounted for 3.4%, 1.9%, and 0.03% of cumulative N2O, respectively. Our results also demonstrated that applying manure compost in combination with urea resulted in lower seed yield-scaled N2O emissions (0.046 g N2O-N kg−1 seed) compared to applying urea alone, even when the total N input was identical, suggesting the potential of manure compost in a more sustainable agriculture model. The low emission factor of manure compost (0.005%) compared to urea (0.019%) supports this potential, indicating the slow-release nature of manure may mitigate N2O while maintaining crop yield. Additionally, the overall low emissions may be attributed to the relatively high soil pH (8.17), which influences the denitrification product ratio, favoring the conversion of N2O to N2. Our study provides valuable insights into the factors contributing to N2O emissions in integrated crop-livestock farming systems, and highlights the importance of distinguishing multiple sources of N2O emissions to develop effective strategies for reducing emissions while promoting sustainable agriculture.

Technical transformation of heavy/ultra-heavy oil production in China driven by low carbon goals: A review

With the growing global energy demand and limited production of conventional crude oil, the extraction of unconventional heavy and extra-heavy oil is receiving unprecedented attention. China is the fourth largest producer of heavy oil. High viscosity and complex formation environments increase the difficulty of exploiting heavy oil. To alleviate the economic cost and environmental pollution pressure brought by heavy oil production, China has achieved significant advancement in the innovation of heavy oil recovery, including accelerating the transformation of thermal technology to non-thermal technology and the use of new energy technology to achieve the goal of low-carbon economy and green environmental protection. Based on the background of heavy oil in China, this paper recharacterizes the high-viscosity mechanism of heavy oil. From different aspects, including basic principles, main characteristics, applicability, limitations, and challenges, this paper comprehensively reviews existing heavy oil recovery technologies, including thermal recovery, in-situ upgrading, cold recovery, and new energy technologies.Currently, in China, the main target of heavy oil recovery is achieving the “carbon peaking and carbon neutrality” goals, which can be described as low energy consumption, low emission, and low pollution. To facilitate the low-carbon transition in the extraction of heavy/extra-heavy oil and attain environmental sustainability goals, it is essential to increase the proportion of non-thermal factors in thermal recovery technologies, develop efficient in-situ catalysts, adopt clean cold extraction techniques, and advance the development of new energy technologies.

Integrated evaluation of city-level CO2 emissions and sinks from 2010 to 2019: Spatio-temporal patterns in the Yangtze River Economic Belt of China

The intensifying impact of global climate change has led to increased attention being paid to carbon dioxide (CO2) emissions and sinks. However, it remains a challenge to accurately assess and map the spatial distribution of CO2 emissions and sinks. Previous studies have primarily focused on national and provincial level research, leaving city-level studies relatively overlooked. Acknowledging the significance of interregional differences and the crucial ecological and economic aspects of the Yangtze River Economic Belt (YREB), we devised a research framework to analyse spatio-temporal patterns through city-level CO2 emissions and sinks. The city-level CO2 emissions were estimated using provincial data and linear allocation models, while CO2 sinks were estimated based on the areas of ecosystems and their corresponding sink coefficients. The analysis of YERB's spatio-temporal patterns focuses on regional hotspots, outliers, and time-series clustering. The findings show that: (1) Between 2010 and 2019, ecosystems within YREB absorbed approximately 41.17%–46.70% of CO2 emissions, with the increase rate of CO2 emissions being lower than that of sinks. (2) City-level CO2 in Western Region of YREB exhibited low emissions and high sinks, while Eastern Region showed high emissions and low sinks, and these characteristics were intensifying. (3) City-level CO2 emissions and sinks demonstrated a “core-periphery” clustering pattern. (4) The regional CO2 balance was predominantly driven by most cities in Eastern Region. This study provides a comprehensive understanding of CO2 emissions and sinks at the city level and offers valuable insights for developing regional collaborative emission reduction strategies in YREB.

Effects of Low Emission Zones on Air Quality, New Vehicle Registrations, and Birthweights: Evidence from Japan

In October 2003 four contiguous prefectures in Greater Tokyo introduced Low Emission Zones (LEZs) from which diesel trucks and buses without particulate filters have been banned from entering. This paper analyzes the effects of this large-scale intervention on air quality, new vehicle registrations, and birthweights. We use a matching approach to construct a control group comparable to the designated areas in terms of propensity scores based on municipality characteristics during the pre-intervention period and apply a difference-in-differences design. We find evidence that the intervention led to reductions in hourly particulate matter concentrations and the incidence of low birthweights in the Greater Tokyo LEZ relative to the control group. We also find that the LEZs led to increases in registrations of new trucks and buses. This is not the case for passenger cars, which were exempt from the regulations. Our paper provides the first evidence of a significant link between LEZs and reduced incidence of low birthweights.

Carbon footprint analysis of fossil power plants in Bangladesh: measuring the impact of CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${CO}_{2}$$\\end{document} and greenhouse gas emissions

Greenhouse gas (GHG) emissions have increased substantially due to industrialization and the rapid growth in energy demand in Bangladesh. The aim of this study is to conduct a comprehensive carbon footprint analysis of fossil power plants in Bangladesh, focusing on the impact of GHG emissions. We evaluate the carbon footprint of fossil power plants based on their power generation capacity, fuel type, specific emission rates, and global warming potential (GWp) for various GHGs. The emission factor approach has been used in conjunction with the International Panel on Climate Change (IPCC) methodology. Findings of the study indicate that fossil power plants in Bangladesh contribute significantly to the country's overall carbon footprint, with CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${CO}_{2}$$\\end{document} and other GHG emissions being the primary drivers. Furthermore, we evaluate Bangladesh's GHG emissions in comparison with neighbouring countries to determine its position. In terms of greenhouse gas emissions from fossil-based power plants, we find that Bangladesh has relatively low emissions compared to its neighbours and developing countries in Asia. Nevertheless, Bangladesh has witnessed a significant increase in coal-fired power generation in recent years, which has emerged as a significant contributor to emissions. Following an analysis of GHG emissions from fossil fuel power plants, we recommend adopting advanced technologies such as carbon capture and storage (CCS) with improved energy efficiency systems and integrating renewable energy sources into the power generation mix. We conclude our analysis by highlighting the importance of transitioning to cleaner, sustainable energy sources to reduce future carbon emissions.

Utilizing Machine Learning Framework to Evaluate the Effect of Climate Change on Maize and Soybean Yield

In recent years, climate change patterns and extreme weather events have adversely affected agricultural production and have raised concerns about its effect on crop yields. These changes can affect the crop yield in many ways including the changes in the length of the growing season, planting and harvest time windows, precipitation amount and frequency, and the growing degree days, etc. So, it is important to analyze the effect of climate change on yield variability for a better understanding of its effect on the crops. This study aims to analyze the historical monthly county-level data for the state of Ohio to quantify maize (Zea mays) and soybean (Glycine max) yield temporal variability and to study the impact of proposed climate change scenarios on the yield. Machine learning algorithms were used to model the effect of monthly temperature and precipitation levels on yield variability, and to study the effect of climate change scenarios on yield. In addition, yield prediction models were integrated with proposed climate change scenarios for higher and lower emissions scenarios to predict the maize and soybean yield for the year 2100. To model the effect of weather parameters on temporal yield variability, the Random Forest model outperformed with RMSE of 0.61 Mt/ha and R2 of 0.73 for maize and with RMSE of 0.21 Mt/ha, R2 of 0.64 for soybean crop. Maximum temperature for July which has a negative correlation with yield was found to be the most dominant parameter for maize yield followed by the precipitation in August, July, and June which have a positive correlation with maize yield. Precipitation in August was found to be the most dominant weather parameter for soybean yield followed by maximum temperature in July. Based on the projected average increase in Ohio’s temperature and precipitation levels alone, maize yield was predicted to drop by 13.2% or 18.5% respectively due to lower emissions and higher emissions climate change scenario of Ohio. Similarly, a 6.64% and a 9.63% drop is expected in the soybean yield by the year 2100 for both lower emissions and higher emissions climate change scenarios. In economic terms based on current commodity values, this is an astounding loss of 254.7 to 369.7 million USD for maize and 535.9 – 751.1 million USD for soybean crop to the state of Ohio.

Decoupling economic growth from carbon emissions: How have Chinese provinces performed in green growth over time?

Extensive research has shown that it is essential to take environmental responsibilities into careful consideration in economic development. Green growth has thereby become one of the most important topics in environmental economics across the globe no matter people are from academia, public, or private sectors. Even though there are many researchers who have investigated the relationship between economic growth and environmental pollution, the vast majority of research has only focused on the absolute amount of economic growth and carbon emissions. In this study, utilizing national statistics about gross domestic product and carbon emissions by region over decades, I not only provide a concrete and comprehensive picture of the green growth of Chinese provinces over time, but also present multiple indicators to capture the growth of carbon emissions and gross domestic product, as well as the growth rate of green growth. The results show that, in spite of a high GDP growth rate, the green growth rate was negative because the carbon emissions growth rate was much higher than the rate of GDP growth. This work has shed some light regarding how Chinese provinces can balance between high economic growth and low carbon emissions in order to have a more sustainable and environment-friendly development path.

Effects of Multidimensional Carbon-Based Nanomaterials on the Low-Carbon and High-Performance Cementitious Composites: A Critical Review.

Cementitious composites are ubiquitous in construction, and more and more research is focused on improving mechanical properties and environmental effects. However, the jury is still out on which material can achieve low-carbon and high-performance cementitious composites. This article compares the mechanical and environmental performance of zero-dimensional fullerenes, one-dimensional carbon nanotubes (CNTs), two-dimensional graphene oxide (GO), and three-dimensional nano-graphite platelets (NGPs) on cementitious composites. The literature review shows that two-dimensional (2D) GO has the best mechanical and environmental performance, followed by 3D NGPs, 1D CNTs, and 0D fullerenes. Specifically, GO stands out for its lower energy consumption (120-140 MJ/kg) and CO2 emissions (0.17 kg/kg). When the optimal dosage (0.01-0.05 wt%) of GO is selected, due to its high specific surface area and strong adhesion to the matrix, the compressive strength of the cementitious composites is improved by nearly 50%. This study will help engineers and researchers better utilize carbon-based nanomaterials and provide guidance and direction for future research in related fields.

Techno-economic assessment of renewable hydrogen production for mobility: A case study

Renewable hydrogen produced by water electrolysis from intermittent energy sources (i.e. sun and wind) is considered one of the most promising energy carriers for the decarbonisation of the so-called “hard-to-abate” sectors, especially heavy-duty transport and industry. To this reason, several new projects are currently under development and public fundings are widely available to boost the renewable hydrogen market. In this fast-changing scenario, this paper presents the techno-economic analysis of an integrated system where renewable power generation, electrolytic hydrogen production and final hydrogen use are closely integrated within a specific site, allowing higher efficiency as well as a containment of hydrogen production and handling cost. The analysis here presented considers a specific case study that includes a 12.5 MWP photovoltaic plant, a 5 MW electrolyser capable to produce nearly 300 t/yr. of renewable hydrogen, and a compression and storage system, all located in Sardinia (Italy). Differently from previous studies, this work faces the analysis from two opposite perspectives: on the one hand, it allows the assessment of the levelized cost of hydrogen during the whole project’s life, making the results comparable to those from other similar analyses; on the other hand, it presents the calculation of project’s profitability in terms of net present value, internal rate of return and payback time from the perspective of a potential investor. The analysis shows that a whole investment of about 20.3 M€ is required, which allows to achieve a levelised cost of hydrogen of 4.09 €/kg, which can decrease to 2.97 €/kg considering the revenues from selling oxygen and the overproduction of electricity. The potential use of renewable hydrogen in urban public transport (by means of fuel cell electric vehicles) has been considered as well, in comparison with conventional diesel buses. Despite the lower cost for fuel (0.41 €/km for hydrogen buses vs. 0.72 €/km for diesel buses) and maintenance and repair (0.20 €/km vs. 0.27 €/km), as well as the lower CO2 emission (some 76 t/yr. saved per bus), the very high investment cost for hydrogen-fuelled buses (up to 720,000 €) makes hydrogen still not competitive with diesel for urban transport, being hydrogen buses characterised by a whole specific transport cost of 1.45 €/km, to be compared with 1.04 €/km for diesel buses.

Solar Energy's Role in Achieving Sustainable Development Goals in Agriculture

The adoption of solar energy in agriculture has the potential to significantly contribute to achieving multiple Sustainable Development Goals (SDGs), particularly those related to clean energy access (SDG 7), sustainable economic growth (SDG 8), responsible consumption and production (SDG 12), and climate action (SDG 13). This review article examines the various applications of solar energy in agricultural practices, including irrigation, crop drying, greenhouse heating, and powering farm machinery. It analyzes the economic, environmental, and social benefits of transitioning to solar-powered agriculture, such as reduced reliance on fossil fuels, lower greenhouse gas emissions, improved energy security, and increased income for farmers. The article also discusses the challenges and barriers to widespread adoption, including high upfront costs, lack of awareness and technical expertise among farmers, and inadequate policy support. Through a comprehensive review of existing literature and case studies, this article highlights the immense potential of solar energy in transforming the agricultural sector and contributing to sustainable development. It concludes by emphasizing the need for concerted efforts from governments, international organizations, and the private sector to promote and facilitate the integration of solar energy in agriculture, particularly in developing countries where access to clean energy and sustainable farming practices are most crucial for achieving the SDGs.

Justification of design parameters of a two-stage cyclone vortex funnel using computer simulation

It has been established that in mini-feed mills with a productivity of up to 5 t/h, pneumatic transport systems with a cyclone unloader are more promising due to the lower emission of finely dispersed components. However, there are fewer such workshops on the market due to the smaller loading -volume of horizontal mixers on which cyclones are installed compared to vertical mixers that perform the function of cyclone-unloaders, in which emissions of fine dust into the work area are higher, which negatively affects working conditions, health of workers, as well as fire safety of premises. To reduce emissions of fine dust into the exhaust pipe of the cyclone, a technical solution in the form of a two-stage cyclone was proposed. A preliminary experimental test on a laboratory model showed an increase in particle collection by 32% about the serial cyclone-unloader CR under the same initial conditions and in the same standard size. As part of the design of a full-size version of the proposed two-stage cyclone, computer modeling of the separation process was carried out in the FlowSimulation software environment with dimensions close to the commercially produced cyclone BCR-450 to identify the influence of the geometric parameters of the -vortex funnel on the process of separation of fine particles from the transporting air flow with variable geometric dimensions diameter and height of the vortex funnel in the range from 100 to 270 and from 100 to 235 mm, respectively. Preliminary rational geometric parameters of the vortex funnel diameter equal to 180 mm and height equal to 150 mm of the proposed technical solution with a hydraulic resistance coefficient of8.4 are substantiated.

Demand flexibility and its impact on a PEM fuel cell-based integrated energy supply system with humidity control

The development of efficient energy systems and renewable alternative energy sources has attracted substantial attention owing to the rise in environmental deterioration and energy usage. A combined cooling, heating, and power (CCHP) system can fully use different types of energy with low emissions and offers a viable solution to address these issues. Demand flexibility is a key issue in collaborating with different types of supplies and demands for optimal system operation. In this study, a proton-exchange membrane fuel cell driven CCHP system with humidity control is proposed to fully investigate the impact of demand flexibility on system operation. A novel flexible load regulation strategy was designed for the proposed system. The results show that flexible load regulation can reduce the thermal storage capacity by 25 %. Supplement heating and cooling do not need operate in winter and summer. With flexible regulation, the heating load can be reduced by 15.69 % in winter, and the cooling and dehumidification loads can be reduced by 30.71 % and 6.09 % in summer. The reductions in hydrogen consumption can reach 1.98 % and 7.51 % during winter and summer. The proposed method can simultaneously increase the exergy efficiency and reduce carbon emission, as well as the economy cost.

Use of modelling tools to assess climate change impacts on smallholder oil seed yields in South Africa.

Oil seed crops are the second most important field crops after cereals in the agricultural economy globally. The use and demand for oilseed crops such as groundnut, soybean and sunflower have grown significantly, but climate change is expected to alter the agroecological conditions required for oilseed crop production. This study aims to present an approach that utilizes decision-making tools to assess the potential climate change impacts on groundnut, soybean and sunflower yields and the greenhouse gas emissions from the management of the crops. The Decision Support Tool for Agrotechnology Transfer (DSSAT v4.7), a dynamic crop model and the Cool Farm Tool, a GHG calculator, was used to simulate yields and estimate GHG emissions from these crops, respectively. Four representative concentration pathways (RCPs 2.6, 4.5, 6.0, and 8.5), three nitrogen (0, 75, and 150 kg/ha) and phosphorous (0, 30 and 60 P kg/ha) fertilizer rates at three sites in Limpopo, South Africa (Ofcolaco, Syferkuil and Punda Maria) were used in field trials for calibrating the models. The highest yield was achieved by sunflower across all crops, years and sites. Soybean yield is projected to decrease across all sites and scenarios by 2030 and 2050, except at Ofcolaco, where yield increases of at least 15.6% is projected under the RCP 4.5 scenario. Positive climate change impacts are predicted for groundnut at Ofcolaco and Syferkuil by 2030 and 2050, while negative impacts with losses of up to 50% are projected under RCP8.5 by 2050 at Punda Maria. Sunflower yield is projected to decrease across all sites and scenarios by 2030 and 2050. A comparison of the climate change impacts across sites shows that groundnut yield is projected to increase under climate change while notable yield losses are projected for sunflower and soybean. GHG emissions from the management of each crop showed that sunflower and groundnut production had the highest and lowest emissions across all sites respectively. With positive climate change impacts, a reduction of GHG emissions per ton per hectare was projected for groundnuts at Ofcolaco and Syferkuil and for sunflower in Ofcolaco in the future. However, the carbon footprint from groundnut is expected to increase by 40 to 107% in Punda Maria for the period up to 2030 and between 70-250% for 2050, with sunflower following a similar trend. We conclude that climate change will potentially reduce yield for oilseed crops while management will increase emissions. Therefore, in designing adaptation measures, there is a need to consider emission effects to gain a holistic understanding of how both climate change impacts on crops and mitigation efforts could be targeted.

Integrated optimization modelling framework for low-carbon and green regional transitions through resource-based industrial symbiosis

The development and utilization of bulk resources provide the basic material needs for industrial systems. However, most current resource utilization patterns are unsustainable, with low efficiencies and high carbon emissions. Here, we report a quantitative tool for resource-based industries to facilitate sustainable and low-carbon transitions within the regional economy. To evaluate the effectiveness of this tool, the saline Qinghai Lake region was chosen as a case study. After optimizing the industrial structure, the benefits of economic output, resource efficiency, energy consumption, solid waste reduction, and carbon emission reduction can be obtained. The scenario analyses exhibit disparities in different transition paths, where the carbon mitigation, economic output, and resource efficiency that benefit from optimal development paths are significantly better than those of the traditional path, indicating the urgency of adopting cleaner technology and industrial symbiosis for regional industries.

Modelling and analysis of heat pump integrated Photovoltaics-Wind systems for an agricultural greenhouse in Turkey

This study focused on modelling and analysing photovoltaics and wind systems to meet the heating demand of a commercial greenhouse. The aim is to evaluate technical, economic, and environmental performances of the related systems and to determine the optimum configuration. A novel approach was introduced by integrating hybrid energy systems with large-scale wind turbines and developing a dynamic heat transfer model. A large commercial greenhouse with an area of 26,640 m2 located in Izmir, Turkey was selected for considering Mediterranean climate, and a detailed heat transfer model of the greenhouse were developed considering heat transfers by convection, radiation, ventilation, and infiltration. A combination of air source heat pumps, photovoltaic panels and wind turbines were used for meeting the heating demand of the related greenhouse. Five different on-grid energy systems scenarios, namely (i) Photovoltaics-Heat Pump, (ii) Photovoltaics-Wind Turbine- Heat Pump, (iii) Wind Turbine- Photovoltaics- Heat Pump (iv) Wind Turbine- Heat Pump, and (v) only Heat Pump were considered. The system modelling with a detailed heat transfer analysis of the greenhouse was made by MATLAB. The energy analysis of the systems was performed on an hourly basis for one calendar year. The annual heating demand and the corresponding electricity consumption of the greenhouse were calculated as 497.37 and 114.07 kWh/m2, respectively. Net Present Value, Levelized Cost of Energy and CO2 savings were used to evaluate economic and environmental performances of the systems. Among five on-grid energy system scenarios, the first scenario, consisting of 5271 photovoltaic panels and 20 heat pumps, emerged as the most economically attractive choice with Net Present Value and Levelized Cost of Energy of $547,440.40 and 0.080146 $/kWh, respectively. Critical parameters affecting the economy of this scenario were found to be electricity prices, tomato yield, and photovoltaic panel prices. For environmental evaluation the fourth scenario, integrating wind turbines and heat pumps, achieves the highest CO2 savings of 2,064.73 tons due to increased renewable electricity production and lower life-cycle CO2 emissions of wind turbines compared to photovoltaic systems. This analysis enhanced the understanding of energy dynamics in greenhouse environments, contributing to the advancement of sustainable practices in agriculture.

Urban air pollution evaluation in downtown streets of a medium-sized Latin American city using AERMOD dispersion model.

The transport sector is considered the largest contributor of air pollutants in urban areas, mainly on-road vehicles, affecting the environment and human health. Bahía Blanca is a medium-sized Latin American city, with high levels of traffic in the downtown area during peak hours. In this regard, it is necessary to analyze air pollution using an air quality model considering that there are no air pollutant measurements in the central area. Furthermore, this type of study has not been carried out in the region and since the city is expected to grow, it is necessary to evaluate the current situation in order to make effective future decisions. In this sense, the AERMOD model (US-EPA version) and the RLINE source type were used in this work. This study analyzes the variations of pollutant concentrations coming from mobile sources in Bahía Blanca's downtown area, particularly carbon monoxide (CO) and nitrogen oxides (NOx) during the period Jul-2020 to Jun-2022. It is interesting to note the results show the maximum concentration values detected are not directly associated with maximum levels of vehicle flow or emission rates, which highlights the importance of meteorological parameters in the modeling. In addition, alternative scenarios are proposed and analyzed from a sustainable approach. Regarding the scenario analysis, it can be concluded that diesel vehicles have a large influence on NOx emissions. Moreover, restrictions as strict as those proposed for a Low Emission Zone would be less applicable in the city than alternative temporary measures that modify traffic at peak hours.

Developing onshore wind farms in Aotearoa New Zealand: carbon and energy footprints

ABSTRACT In recognition of deeper insights into the implications of wind farm deployments, this paper addresses the need for an updated Life Cycle Assessment (LCA) for onshore wind generation systems, using 4.3 MW wind turbines and direct drive permanent magnet synchronous generators. The environmental and energy performances were estimated through an LCA for an onshore wind plant under construction in Aotearoa New Zealand with a total nameplate capacity of 176 MW. This study used real construction data showing literature data overestimates civil works and underestimates transportation contributions in the wind farm footprint. Further, different end-of-life management alternatives for turbine blades are analysed: landfill, mechanical recycling, and chemical recycling. The results indicate a carbon footprint of 10.8–9.7 gCO2eq/kWh, a greenhouse gas payback time of 1.5–1.7 years for avoided combined cycle gas turbines, and an energy payback time of 0.4–0.5 years, in which the chemical recycling of the blades is the lower emission solution overall. The outcomes underscore the environmental efficiency of onshore wind farms and their important role in the energy transition. Notably, the manufacturing of wind turbines is the primary contributor to the carbon and energy footprints, highlighting a critical area for targeted environmental mitigation strategies.

The Impact of Increasing Traffic Volume on Autonomous Vehicles in Roundabout

This study employs the PTV VISSIM simulation software to investigate the impact of increasing traffic volumes on conventional vehicles and autonomous vehicles (AVs) with distinct behavioural traits: cautious, normal, and aggressive. The simulations cover a range of traffic volumes, from 100 to 600 vehicles, and measure the effects on travel time, emissions (CO, NOX, VOC), and fuel consumption. The results show that with increasing penetration rates of AVs, travel times generally decrease, with aggressive AVs achieving the shortest times, followed by normal, then cautious AVs. Emissions and fuel consumption also tend to decrease as the penetration rate of AVs increases. Notably, the results demonstrate that aggressive AVs excel in reducing travel time, while normal AVs consistently balance between efficiency and reduced emissions, and cautious AVs emphasize safety and lower emissions. Despite the differing behavioural traits, all AV types exhibit a marked improvement over conventional vehicles in terms of travel time, emissions, and fuel consumption. At every penetration rate, AVs lead to shorter travel times and lower emissions, with aggressive AVs being the most efficient, followed by normal and then cautious AVs. These findings emphasize the potential benefits of integrating autonomous vehicles into transportation networks. They suggest that optimizing AV behaviour, depending on the context and objectives, can lead to more efficient, environmentally friendly traffic systems. The study offers valuable insights for policymakers, urban planners, and researchers aiming to leverage the distinct strengths of each AV behaviour to create a more sustainable and efficient future for autonomous transportation.

Desirability-based optimization of dual-fuel diesel engine using acetylene as an alternative fuel

The study examined the dual-fuel engine performance employing acetylene gas as primary fuel and diesel as pilot fuel. The engine's operational parameters were adjusted using the Box-Behnken design, and the results were recorded. The best operating settings were yielded as 81.25 % engine load, 4.48 lpm acetylene gas flow rate and the compression ratio were 18. At this optimized setting the BTE was 27.1 % and the engine emitted 360 ppm of NOx, 56.2 ppm of HC, 104 ppm of CO. The experimental data at optimized setting was compared to the optimized results, and the percentage of errors was within 7 %. Two advanced machine learning methods, LightGBM and Tweedie, were used to predict engine efficiency and emissions. Tweedie-based models had an R2 value of 0.89–1, while LightGBM-based models had an R2 value of 0.38–1. The mean squared error was 0.24–45.04 for Tweedie-based models and 8.5 to 153.89 for LightGBM-based models. On the basis of R2 and MSE, it was observed that Tweedie was superior at making predictions than LightGBM. The study demonstrated the efficient functioning of a dual-fuel engine using acetylene as an alternative fuel for increased performance and lower emissions.