To accomplish IMO's emissions reduction targets, the Chinese government has established emission control areas and implemented strict sulfur limitation policies. Faced with the downturn in the shipping industry and the challenge of insufficient supply of compliant fuel, Hong Kong and Shenzhen in China have implemented different low-sulfur fuel subsidy policies. Therefore, it is particularly important to study the air pollution control mechanism considering low sulfur fuel subsidy policy. Firstly, the non-cooperative game models considering low-sulfur fuel subsidies are constructed. Secondly, the mechanisms of various factors affecting port pricing, throughput and profit are analyzed. Then, a case study is conducted by using AIS data of container ships in Shanghai Port and Ningbo Zhoushan Port. The study reveals that whether the container service price of the leader port increases or decreases, the follower port always take corresponding follow-up measures. In both sequential and simultaneous game, the profit difference of the leader port is positively related to the subsidy ratio of the port’s own and negatively related to the coefficient of the competitor's subsidy. In conclusion, the low-sulfur fuel subsidy policy has a significant positive impact on the step-by-step implementation of more stringent pollution reduction policies in port waters.

A scientific carbon accounting system can help enterprises reduce carbon emissions. This study took the enterprise in the Yangtze River basin as a case study. The accounting classification of carbon emissions in the life cycle of lime production was assessed, and the composition of the sources of carbon emission was analyzed, covering mining explosives, fuel (diesel, coal), electricity and high temperature limestone decomposition. Using the IPCC emission factor method, a carbon life cycle emission accounting model for lime production was established. We determined that a total of 354,200 tons of carbon emissions were generated from producing 321,000 tons of lime in a year. The decomposition of limestone at a high temperature was the largest carbon emission source, accounting for 62.34% of the total carbon emission. Coal combustion was also an important source of carbon emissions, accounting for 32.66% of total carbon emissions. The carbon emission for one ton of activated lime was 1.46kg, 1.49kg, 1100kg and 1102.95kg respectively for the open-pit, mining stage, crushing stage, and the calcination stage. Based upon the main sources of carbon emission for lime production, carbon emission reduction should focus on CO2 capture technology, fuel optimization and energy control. Using our analysis of carbon emissions, the carbon emission factor of producing unit quicklime of the lime enterprise in the Yangtze River basin was determined. Furthermore, the research into carbon emission reduction for lime production can provide a point of reference for the promotion of carbon neutrality in the same industry.

The global transport sector of the world economy contributes about 15% of the Greenhouse Gas (GHGs) emissions in the world today. The University of Saskatchewan has pursued the green energy transition over the years. They have spearheaded diverse sustainability projects and agendas, due to the importance of curbing climate change and advancing sustainability. The transport system in the university campus is one area of focus where the Sustainability Office plans to introduce some innovations, as a way of curbing GHG emissions while also advancing sustainability practice in the university campus. The study carried out an economic benefit analysis on the campus fleet (consisting of 91 ICE vehicles) to determine if it is economically or financially feasible to transition from Internal Combustion Engine (ICE) or PVs (Petrol Vehicles) to Electric Vehicles (EVs). The analysis used RETScreen Expert software for analyzing renewable energy technology projects. The variables of Payback Period (PBP), cash flow projections, savings made from transitioning (fuel cost savings and energy cost savings), Benefit-Cost-ratio, GHG emission reduction potential, etc. were analyzed. The findings revealed that the GHG emission from the campus fleet will be reduced by 100% (this will result in the removal of about 298.1 tCO₂ from the environment). Also, the fleet manager will save approximately $129,049 (88.9%) in fuel costs. Apart from these, the return on investment will be achieved in year 5 (all things being equal), but can be reduced to year 2 if the vehicles are put into constant and active use (eliminating most idle times. Also, the Sustainability Office will be making a GHG reduction revenue of $14,906.

Integrated photovoltaic-fuel cell (IPVFC) systems, amongst other integrated energy generation methodologies are renewable and clean energy technologies that have received diverse research and development attentions over the last few decades due to their potential applications in a hydrogen economy. This article systematically updates the state-of-the-art of IPVFC systems and provides critical insights into the research and development gaps needed to be filled/addressed to advance these systems towards full commercialisation. The design methodologies, renewable energy-based microgrid and off-grid applications, energy management strategies, optimisations and the prospects as self-sustaining power source were covered. IPVFC systems could play an important role in the upcoming hydrogen economy since they depend on solar hydrogen which has almost zero emissions during operation. Highlighted herein are the progresses as well as the technical challenges requiring research efforts to solve to realise numerous potential applications of IPVFC systems such as in unmanned aerial vehicles, hybrid electric vehicles, agricultural applications, telecommunications, desalination, synthesis of ammonia, boats, buildings, and distributed microgrid applications.

Turning ships in port turning basins is an important and responsible operation, mainly involving the ship itself and the port tugs. Such operations involve many maneuvers that consume a lot of energy (fuel) and emit a lot of emissions. Turning basins in harbors and quay approaches are in most cases relatively shallow. The paper examines the turning of ships in port turning basins using harbor tugs, the effect of shallow depth on ship turning, energy (fuel) consumption and the generation of emissions during such maneuvers of harbor tugs. The paper presents the developed theoretical models, and the experimental results on theoretical models, which were verified on real ships and using calibrated simulators. Discussions and conclusions prepared on the basis of the research base. The use of the developed methodology makes it possible to increase shipping safety, optimize maneuvers and reduce energy (fuel) consumption when turning ships in the port and at the same time reduce the amount of fuel consumed by port tugs and reduce the number of emissions of tugs during such operations.

The purpose of this research was to analyze the moderating effect of clean energy innovation on the relationship between corporate carbon footprint and corporate profits in those industrial sectors associated with high demand for fossil fuels in which it is "hard to abate" CO₂e emissions. It used a longitudinal research design, in particular a panel study under a structural equation modeling (SEM) approach, based on partial least squares. For the longitudinal moderation analysis, this research employed the Bayesian method by defining a multiple-indicator latent growth curve model (B-LGC model). A global sample was used consisting of 7,827 firm-year observations between 2015 and 2021 corresponding to 167 international firms. The results revealed a very significant impact of the corporate carbon footprint on corporate profits. Likewise, the results showed that innovations in clean energy, when measured as the consumption of renewable energy, positively moderates the relationship between the greenhouse gas emissions from the value chain associated with Scope 3 of the Greenhouse Gas (GHG) Protocol, and the gross profit margin obtained. Besides the academic contribution represented by the moderating effect of clean energy innovation, these findings imply that a more detailed understanding of the emissions of the entire value chain (Scope 3 CO₂e) by executives and managers of high emitting CO₂e companies represents an effective mechanism to obtain higher profits, create competitive advantages and, at the same time, achieve a net zero emissions strategy. More importantly, public policy makers will be able to use these results to revise CO₂e-related policies paying more attention to the Scope 3 CO₂e emissions produced by these companies, to formulate regulatory and control mechanisms that stimulate clean energy innovation.

Air pollution is a leading cause of death worldwide, and has a profound impact on the planet's climate and ecosystems. A substantial portion of air pollution is attributable to Ocean Going Vessels (OGVs). In light of this, international regulations have been put in place to mitigate air pollutant emissions from OGVs. While studies have indicated that these regulations can create significant health, environmental, and economic benefits, there remains a research gap regarding their specific impact on enhancing air quality. The aim of this study is to investigate how the implemented regulations have affected air quality in the Southern North Sea. The study found that the international regulations on ship emissions have successfully led to a decline in SO2 emissions from OGVs in the Southern North Sea, which resulted in a reduction of ambient SO2 concentrations inland, leading to positive effects on public health and the environment. However, it was projected that their proportion is tend to increase in the following years. Moreover, the study revealed that Exhaust Gas Cleaning Systems (EGCS) present significant concerns. They were significantly more frequently found to be non-compliant, and more alarmingly, they emit notably higher average levels of SO2. It also turned out that international regulations in the southern North Sea have less effect on the reduction of NOx emissions from OGVs than expected. The NOx emissions from OGVs will even account for an alarming 40% of the total domestic NOx emissions for the Belgian Region by 2030.

Alternative fuels containing biocomponents produced in various technologies are introduced in aviation to reduce its carbon footprint but there is little data describing their impact on the performance and emissions of engines. The purpose of the work is to compare the performance and gas emissions produced from two different jet engines: the GTM-140 microturbine and the full-size DGEN380 turbofan, powered by blends of Jet A-1 and one of two biocomponents: 1) ATJ and 2) HEFA produced from used cooking oil (UCO) in various concentrations. The acquired data will be used to develop an engine emissivity model to predict gas emissions. Blends of the mineral fuel with synthetic components were prepared in various concentrations, and their physicochemical parameters were examined in the laboratory. Measurements of emissions from both engines were carried out in selected operating points using the Semtech DS gaseous analyzer and the EEPS spectrometer. The impact of tested blends on engine operating parameters is limited, and their use does not carry the risk of a significant decrease in aircraft performance or increase in fuel consumption. Increasing the content of biocomponents causes a noticeable rise in the emission of CO and slight increase for some other gasses (HC and NOx), which should not, however, worsen the working conditions of the ground personnel. This implies that there are no contraindications against using tested blends for fuelling gas-turbine engines.

In order to assess the human exposure risks from the release of contaminants from water pipes made of polyvinyl chloride (PVC), experiments were carried out by subjecting the PVC pipe material to burning and leaching conditions followed by analysis of the emission and leachate samples. The emissions of burning pipes were analyzed by both infrared spectrometry and gas chromatography-mass spectrometry (GC-MS). The emission results indicate the presence of chlorinated components including chlorine dioxide, methyl chloride, methylene chloride, allyl chloride, vinyl chloride, ethyl chloride, 1-chlorobutane, tetrachloroethylene, chlorobenzene, and hydrogen chloride were detected in the emissions of burning PVC pipes. Furthermore, the concentrations of benzene, 1,3-butadiene, methyl methacrylate, carbon monoxide, acrolein, and formaldehyde were found at levels capable of affecting human health adversely. The analysis of PVC pipe leachates using GC-MS shows that there are 40-60 tentatively identified compounds, mostly long-chain hydrocarbons such as tetradecane, hexadecane, octadecane, and docosane, were released when the burned PVC materials were soaked in deionized water for one week. Quantitative analysis shows that 2-butoxyethanol, 2-ethyl-1-hexanol, and diethyl phthalate were found in the burned PVC polymer at the average levels of 2.7, 14.0, and 3.1 micrograms per gram (g/g) of pipe material. This study has significant implications for understanding the benzene contamination of drinking water in the aftermath of wildfires that burned polymer pipes in California.

Three physical elements are missing from the conventional formalism of quantum photonics: 1) the quantum Rayleigh spontaneous and stimulated emissions; 2) the unavoidable parametric amplification; and 3) the mixed time-frequency spectral structure of a photonic field which specifies its duration or spatial extent. As a single photon enters a dielectric medium, the quantum Rayleigh scattering prevents it from propagating in a straight-line, thereby destroying any possible entanglement. A pure dynamic and coherent state composed of two consecutive number states, delivers the correct expectation values for the number of photons carried by a photonic wave front, its complex optical field, and phase quadratures. The intrinsic longitudinal and lateral field profiles associated with a group of photons for any instantaneous number of photons are independent of the source. These photonic properties enable a step-by-step analysis of the correlation functions characterizing counting of coincident numbers of photons or intensities with unity visibility interference, spanning the classical and quantum optic regimes

Cement manufacturing is one of the most energy-intensive industries in the world. Most of the cost of producing cement is accounted by fuel consumption and power expenditures. Thermal power plants are the major source of electricity in Pakistan. But they are not efficient and environmentally friendly. This study simulates four different models for five cement plants of Pakistan on Homer Pro software and compares the optimal solutions based on the net present cost (NPC), levelized cost of electricity (LCOE) and greenhouse gas (GHG) emissions. Model-1 consists of solar panels, electrolyzer, hydrogen tank, hydrogen generator and converter. Model-2 has only a diesel generator and acts as a base case in this study. Model-3 has solar panels and a battery-converter system. In Model-4, diesel generators, solar panels and converters are considered. Based on NPC, the most optimal model is Model-4, having a 0.249 $/KWh LCOE in islanded systems. The NPC and operating costs are US$540 million and US$ 32.5 million per year, respectively, with a 29.80% reduction in CO2 emissions when compared to the base case. Based on GHG emissions, Model-1 and Model-3 are the best models with 0% GHG emissions. Sensitivity analyses is also performed using the parameters of load, inflation rate and discounted rate. The results prove that the proposed hybrid micropower systems (HMS) can sustainably provide electricity for 24 hours a day to the sites under consideration with minimum objectives.

Primary energy consumption is one of the key drivers of global CO₂ emissions that, in turn, heavily depend on the efficiency of involved technologies. Either the improvement in technology efficiency or the expansion of non-fossil fuel consumption require large investments. The planning and financing of such investments, by policy makers or global energy firms, require, in turn, reliable measures of associated global spreads and their evolution in time. In this paper, our main contribution is the introduction of index measures for accessing global spreads (that is, measures of inequality or inhomogeneity in the statistical distribution of a related quantity of interest) of technology efficiency and CO₂ emission in primary energy consumption. These indexes are based on the Gini index, as used in economical sciences, and generalised entropy measures. Regarding primary energy sources, we consider petroleum, coal, natural gas and non-fossil fuels. Between our findings, we attest some stable relations in the evolution of global spreads of technology efficiency and CO₂ emission, and a positive relation between changes in global spreads of technology efficiency and use of non-fossil fuel.

In this paper a dynamic relationship between the CO2 emissions in Finland, Norway and Sweden is presented. With the help of a VAR(2) model, and using the Granger terminology, it is shown that the emissions in Finland are affecting those in Norway and Sweden. Other aspects of this dynamic relationship are presented as well.

A testable hypothesis with quantitative predictions is put forward, proposing that strong acids produced on land at a rate correlated with global population are an increas-ing cause of increasing atmospheric CO2 pressure (pCO2). This hypothesis widens the imbalance assumed between the global uptake of CO2 by photosynthesis and emissions of CO2 from all sources, including the combustion of fossil fuels, causing greater atmos-pheric pCO2. Our modelling tests for reactions of dissolved inorganic carbon show that increasing atmospheric CO2 is caused thermodynamically by falling land surface pH values, but only with bicarbonate present. Strong acids generated by oxygen from re-duced inorganic compounds of nitrogen and sulphur emit CO2 almost stoichiometrically from dissolved bicarbonate in the pH range of most global soils, between 6 to above 8. We show that each decrease of aqueous pH value of 0.01 units from acidification of sur-face water near pH 8 potentially increases the pCO2 in the atmosphere by about 7 ppmv. The surface pH value of the ocean near 8.10 in 2000 has been shown to decrease at this rate, a result of equilibrating with the rising atmospheric pCO2. Therefore, our qualita-tive estimates of increasing atmospheric CO2 driven by irreversible acidification on land are critical and need broad scale validation. Unfortunately, these acidifying processes on land from excessive nitrogen fertiliser, export of alkaline produce from farmlands and other processes were not considered by previous biogeochemical reviews. However, several very recent research studies showing CO2 emissions from soil during nitrifica-tion are consistent with our hypothesis. Despite counter measures to curb excessive CO2 emissions, including carbon capture and geological storage, the increasing Keeling curve for pCO2 may continue to rise because zero carbon policies do not address these causes. We recommend that this hypothesis be further tested by in situ experiments in neutral soils and water, designed to compare CO2 emissions under the acidifying conditions de-scribed in this article. Corrective counter measures can then be designed and applied.

A cross-flow ultrafiltration harvesting system of a pre-concentrated microalgae culture was tested in an innovative anaerobic-based WRRF. The microalgae culture was cultivated in a membrane photobioreactor fed with the effluent from an anaerobic membrane bioreactor treating sewage. These harvested microalgae biomass was then anaerobically co-digested with primary and secondary sludge from the water line. Depending on the needs of this anaerobic co-digestion, the filtration harvesting process was evaluated intermittently over a period of 212 days for different operating conditions, mainly the total amount of microalgae biomass harvested and the desired final total solids concentration (up to 15.9 g·L1 with an average of 9.7 g·L1). Concentration ratios of 15-27 were obtained with average transmembrane fluxes ranged from 5 to 28 L·m2·h1. Regarding membrane cleaning, both backflushing and chemical cleaning resulted in transmembrane flux recoveries that were, on average, 21% higher than those achieved with backflushing alone. The carbon footprint assessment shows promising results as the GHG emissions associated with the cross-flow ultrafiltration harvesting process could be less than the emissions savings associated with the energy recovered from the biogas production from the anaerobic valorisation of the harvested microalgae.

Additive Manufacturing (AM) has been proving suitable to support or even replace traditional manufacturing in several industries, offering many advantages such as delivery time and reduction in terms of material waste, energy consumption and greenhouse gas (GHG) emissions. This study aimed to carry out a comparative assessment of the life cycle, from gate to gate, in the production of a low alloy carbon steel flange part using ER-90 wire. The methods utilized were Wire and Arc Additive Manufacturing (WAAM) and conventional manufacturing (CM) by forging, and comparative factors were energy demands, GHG emissions and generated solid waste. The total energy consumption in WAAM was 10,239.40 MJ, total carbon footprint in CO2 equivalent (CO2e) was 714.1 kgCO2e kg-1, and generated solid waste was 68.6 kg, respectively, 90%, 95% and 76% lower than consumption calculated in conventional manufacturing.

This study investigates the effect of oil viscosity on pollutant emissions and fuel consumption of an internal combustion engine (ICE) in high-altitude using Response Surface Methodology (RSM). A Chevrolet Corsa Evolution 1.5 SOHC gasoline engine was used in Cuenca, Ecuador (2560 meters above sea level), testing three lubricating oils with kinematic viscosities of 9.66, 14.08, and 18.5 cSt under various engine speeds and loads. Key findings include: hydrocarbon (HC) emissions were minimized to 7.25 ppm with low viscosity and load; carbon dioxide (CO2) emissions peaked at 15.2% vol with high viscosity and load; carbon monoxide (CO) ranged from 0.04% to 3.74% depending on viscosity and load; nitrogen oxides (NOx) were significantly influenced by viscosity, RPM, and load, indicating a need for model refinement; and fuel consumption was significantly affected by load and viscosity. RSM-based optimization identified optimal operational conditions with a viscosity of 13 sCt, 1473 rpm, and a load of 78%, resulting in 52.35 ppm of HC, 13.97% vol of CO2, 1.2% vol of CO, 0 ppm of NOx, and a fuel consumption of 6.66 l/h. These conditions demonstrate the ability to adjust operational variables to maximize fuel efficiency and minimize emissions. This study underscores the critical role of optimizing lubricant viscosity and operational conditions to mitigate environmental impact and enhance engine performance in high-altitude environments.

The goal of this study was to evaluate the feasibility of methods based on the Worldwide Harmonized Light-Duty Vehicles Test Procedure Brake Cycle to measure brake wear particle emissions for currently used electrified vehicles. Our results indicated that reducing brake friction work reduced brake wear particle emissions. Commercially available, plug-in hybrid electric vehicles reduced emissions by 85% for PM₁₀, 78% for PM_2.5, and 87% for particle numbers (PNs) compared to vehicles with internal combustion engines. Brake friction work showed a linear relationship with PM₁₀ and PM_2.5. Nanoparticle PM emissions tended to increase slightly with regenerative braking, but did not contribute significantly to the overall PM percentage. the emission events of high number concentrations of nuclei mode particles (<20 nm in diameter) in electric vehicle brake assemblies designed for regenerative braking use under high-temperature, high-load braking conditions with full-friction brakes. The nuclei mode particles amplified the PN emissions and led to a high variability. In strict regulatory certification tests, where measurement reproducibility and stability are required, it is appropriate to measure PNs under brake conditions appropriate for the actual use of electric vehicles rather than under full-friction brake conditions or to remove particle measurements smaller than 20 nm.

Cement industry is one of the highest energy consuming industries. The quantity of fuel and energy needed accounts for most of the cost of cement manufacturing. Thermal power plants generate electricity but are harmful and ineffective by nature. As a backup mechanism to account for main grid failures, batteries can be utilized. In this paper the first ever investigation on battery’s depth of discharge (DOD) for four different kinds of battery technologies is carried out in the framework of cement industry. The intended battery technologies are the lead-acid battery (LA), lithium-ion battery (Li-ion), vana-dium redox battery (VR), and nickel–iron battery (Ni-Fe). Four hybrid energy generation models (HEGMs) for five cement plants of Pakistan using the HOMER pro software are proposed. Cement plants includes Askari Cement Plant, Wah (ACPW); Bestway Cement Plant, Kalar Kahar (BCPKK); Bestway Cement Plant, Farooqia (BCPF); Bestway Cement Plant, Hattar (BCPH); and DG Cement Plant, Chakwal (DGCPC). HEGM-1 comprises of a diesel generator (DGen), a photovoltaic system (PV), a converter, and a battery system. HEGM-2 comprises of a PV system, a converter, and a battery system. HEGM-3 is the grid-connected version of HEGM-1 and HEGM-4 is the grid-connected version of HEGM-2. A base-model consisting of grid only is used as a reference. A multi-criteria decision analysis (MCDA) is performed by formulating a cumulative objective function (COF) which includes net present cost (NPC), levelized cost of energy (LCOE), and greenhouse gas (GHG) emissions. The principal objective is the maximization of COF while simultaneously minimizing the objectives (NPC, LCOE and GHG emissions), based on optimal battery technology and DOD. The results reveal that VR is the most suitable battery technology with 10% DOD. It is achieved for DGCPC with HEGM-3 with 61.49% of NPC, 78.62% of LCOE and 84.00% of GHG emissions reduction as com-pared to the base model.

The paper presents data from a monthly campaign for the elemental composition of PM10 in a specific receptor Kotórz Mały (Opole Voivodeship) located in the vicinity of a moderately inhabited rural area, measured in one-hour samples with the use of the PX-375 analyzer by the Horiba company. The hourly variability of SO2, NO, NO2, CO, O3 concentrations as well as the variability of meteorological parameters were also determined. On average, during the entire measurement period, the elements related to PM10 can be arranged in the following order: As<V<Ni<Pb<Cr<Mn<Cu<Ti<Zn<K<Fe<Ca<Al<Si<S. Trace elements, including toxic elements, such as As, V, Ni, Pb, Cr, Mn, were present in low concentrations, not exceeding 10 ng/m3 (average daily value). These elements had fairly even concentrations, daily and hourly. The concentrations of the main elements in the PM10 in the receptor are subject to strong hourly changes related not only to changes in the structure of the sources identified in the statistical analysis but mainly to wind speed and direction changes (soil and sand particles pick-up and inflow of pollutants from coal combustion). It has been shown that the transport emission in the receptor can have an intense effect on PM10 in the afternoon.

Many tasks in a modern household are done by machines like e.g. a dishwasher or a vacuum cleaner and in the near future most household tasks can be done by smart service robots. This relieves the residents who in turn can enjoy their free time. This newly gained free time turns out to cause the so called spare time rebound effect due to more resource consumption [1,2]. We roughly quantify this rebound effect and propose a CO2 -budget model to reduce or even avoid it. In modern industry, automation and AI take over work from humans, leading to higher productivity of the company as a whole. This is the main reason for economic growth which leads to environmental problems due to higher consumption of natural resources. We show that, even though the effects of automation at home and in the industry are different (free time versus higher productivity) in the end they both lead to more resource consumption and environmental pollution. We discuss possible solutions to this problem such as carbon taxes, emissions trading systems and a carbon budget.

Understanding speech in noise is particularly difficult for individuals occupationally exposed to noise due to a to a mix between noise-induced auditory lesions and energetic masking of speech signal. For years, the monitoring of conventional audiometric thresholds has been the usual way to check and preserve the auditory function. But the highlighting of supra-threshold deficits, notably speech in noise understanding difficulties, has pointed out the need for new monitoring tools. The present study aims to identify the most important variables that predict speech in noise under-standing in order to suggest a new way of regular monitoring. Physiological (distortion products of otoacoustic emissions, electrocochleography) and behavioral (amplitude and frequency modula-tion detection thresholds, conventional and extended high frequency audiometric thresholds) variables were collected in a population of individuals presenting a relative homogeneous occu-pational noise exposure. Those variables were used as predictors in a statistical model (random forest) to predict scores of three different speech in noise tests and a self-report of speech in noise ability. The extended high frequency threshold appears to be the best predictor and therefore an interesting candidate for a new way of monitoring noise exposed professionals.

A controlled ammonia (NH₃) release experiment was performed at a grassland site to quantify the effect of dry deposition, at the field scale between the source and the receptors (NH₃ measurement locations), on the estimates of emission rates by means of inverse dispersion modelling. NH₃ was released for 3 hours at a constant rate of Q = 6.29 mg s⁻¹ from a grid of 36 orifices spread over an area of 250 m². The increase in line-integrated NH₃ concentration was measured with open-path optical miniDOAS devices at different locations downwind of the artificial source. Using a backward Lagrangian stochastic (bLS) dispersion model (bLSmodelR), the fraction of the modelled release rate to the emitted NH₃ (Q_bLS/Q) was calculated from the measurements of the individual instruments. Q_bLS/Q was found to be systematically lower than 1, on average between 0.69 and 0.91, depending on the location of the receptor. We hypothesized that NH₃ dry deposition to grass and soil surfaces was the main factor responsible for the observed depletion of NH₃ between source and receptor. A dry deposition algorithm based on a deposition velocity approach was included in the bLS modelling. Model deposition velocities were evaluated from a ‘big‑leaf’ canopy resistance analogy. Canopy resistances (generally termed Rc) that provided Q_bLS/Q = 1 ranged from 75 to 290 s m⁻¹, showing that surface removal of NH₃ by dry deposition can plausibly explain the original underestimation of Q_bLS/Q. The inclusion of a dry deposition process in dispersion modelling is crucial for emission estimates, which are based on concentration measurements of depositing tracers downwind of homogeneous area sources or heterogeneously distributed hot spots, such as e.g. urine patches on pastures in the case of NH₃.

Artificial Intelligence (AI) is poised to revolutionize the architectural design and energy management of green buildings, offering significant advancements in sustainability and efficiency. This paper explores the transformative impact of AI on improving energy efficiency and reducing carbon emissions in commercial buildings. By leveraging AI algorithms, architects can optimize building performance through advanced environmental analysis, automation of repetitive tasks, and real-time data-driven decision-making. AI facilitates precise energy consumption forecasting and integration of renewable energy sources, enhancing the overall sustainability of buildings. Our study demonstrates that AI can reduce energy consumption and CO2 emissions by approximately 8% and 19%, respectively, in typical mid-size office buildings by 2050 compared to conventional methods. Further, the combination of AI with energy efficiency policies and low-emission energy production is projected to yield reductions of up to 40% in energy consumption and 90% in CO2 emissions. This paper provides a systematic approach for quantifying AI's benefits across various building types and climate zones, offering valuable insights for decision-makers in the construction industry.

The increasing social pressure for decarbonization has placed businesses under considerable scrutiny to actively reduce carbon emissions. A critical step towards achieving this objective is to shift conventional production and consumption systems to more sustainable alternatives. Thus, there is the emergent need to understand the patterns and drivers of the transformative business models (BMs) that underpin that shift. This study adopts a mixed-methods approach that integrates different literature streams--including Sustainability Transitions Theory (STT), Strategic Niche Management (SNM), and the Business Models approach--and stakeholders’ interviews to investigate the key elements of business models that lead towards sustainable practices. This research examines the organizational arrangements of European start-ups operating between 2014 and 2020. The transformation towards decarbonized production and consumption is characterized by efficient combination of business strategies that incorporate advanced technologies (ATs), such as artificial intelligence (AI), machine learning (ML) and its algorithms, along with sustainable elements, resulting in transformative business models. By exploring the driving elements behind the transition to low-carbon approaches, this study fills a significant gap in the existing literature on business models. The findings from this research also hold relevance for policymakers to promote decarbonization.

The building sector has a big potential to reduce the material resource demand needed for building construction and therefore, greenhouse gas (GHG) emissions. Digitalisation can help to make use of this potential and improve sustainability throughout the entire building’s life cycle. One way to address this potential is through the integration of Life-Cycle Assessment (LCA) into the building process by employing Building Information Modelling (BIM). BIM can reduce the effort needed to carry out an LCA and therefore facilitate the integration into the building process. A review of current industry practice and scientific literature shows two main approaches to address BIM-LCA integration. Either the LCA is performed in a simplified way at the beginning of the building process, or it is done at the very end when all the needed information is available, but it is too late for decision-making. One reason for this is the lack of methods, workflows and tools to implement BIM-LCA integration over the entire building process. Therefore, the main objective of this study is to develop an integrated BIM-LCA workflow implemented into a method for the whole building process using an existing structure for cost estimation. A tool is created and used in a case study in Switzerland to test the developed approach. The results of this study show that LCA can be performed continuously in each building phase over the entire building process using existing BIM modelling techniques. The main benefit of this approach is that the re-work caused by the need for re-entering data and the usage of many different software tools that characterise most of the current LCA practices is minimised. Furthermore, decision-making, both at the element and building levels, is supported.

Accurate prediction of mercury content emitted from fossil-fueled power stations is of utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model include coal characteristics and the operational parameters of the boilers. The dataset has been collected from 82 power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of ANFIS-PSO model, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1%, which confirm the accuracy of the model to deal nonlinearity and representing the dependency of flue gas mercury content into the specifications of coal and the boiler type.

The objective of this study was to identify the factors determining a company’s corporate governance related to climate change. We analyzed the effect of various sustainability corporate governance variables on the disclosure level of climate change governance. These variables included facts such as having a dedicated sustainability executive and board committee, the mediating effect of female representation on the board of directors, number of reporting years according to TCFD, membership in a sustainability index, MSCI ESG rating, the existence of a corporate climate transition plan, a mention of the UN Global Compact and GRI, company location, as well as company size and profitability. By adopting a multi-theoretical framework that included stakeholder theory as well the legitimacy and agency theory, the underlying research study used a sample of 100 of the largest global companies by market capitalization and their reporting for the year 2020. Based on 1,400 observations for fiscal year 2020 and using correlation analysis, univariate and linear multiple regressions, we find a positive association between having a climate transition plan in place, being a leader in sustainability according to MSCI ratings, and being a DJSI constituent and the propensity to disclose information on governance for climate change. In addition, we find a company with a dedicated sustainability executive show an increased tendency to be transparent on climate governance issues. Furthermore, having a company location in a developed country is significantly and positively associated with climate change governance. Surprisingly, gender diversity in the corporate board or having a sustainability board committee did not show any significant correlation between a higher climate change governance level. The same was true for companies being active in either the extractive or non-extractive sector. Companies referring to the Global Reporting Initiative (GRI) or UN Global Compact also did not score higher in climate change governance. Neither did corporate profitability or size play a significant role. Our results are robust to variations and provide valuable insights for researchers, academics, executives, practitioners as well as regulators. As more and more companies are shifting towards a climate change reporting framework, it is of paramount importance that we are able to determine the contributing variables that lead to effective climate change corporate governance. Our results are inconsistent with stakeholder theory and are strongly suggesting that a diversified board and the existence of a sustainability committee that meets often/sufficiently may not necessarily lead to a higher level of transparency/quality regarding climate change. While more research is needed, knowing that a dedicated sustainability executive as well as having a climate plan in place can make a difference in climate change reporting, can be very beneficial to many corporate stakeholders. Given the current urgent climate change situation and the crucial role that corporation play in it, dedicated sustainability positions and committees need to be established. The findings could be useful for managers as well as governmental standards setter and regulators who are interested in improving corporate practices dealing with climate change. This study applies STATA software with various regression models to empirically test the relationship between CG and other variables and corporate climate change reporting.