Opinion: Coordinated Development of Emission Inventories for Climate Forcers and Air Pollutants

Emissions into the atmosphere of fine particulates, their precursors, and precursors to tropospheric ozone, not only impact human health and ecosystems, but also impact the climate by altering Earth’s radiative balance. Accurately quantifying these impacts across local to global scales, historically, and in future scenarios, requires emission inventories that are accurate, transparent, complete, comparable, and consistent. In an effort to better quantify the emissions and impacts of these pollutants, also called short-lived climate forcers (SLCFs), the Intergovernmental Panel on Climate Change (IPCC) is developing a new SLCF emissions methodology report. This report would supplement existing IPCC reporting guidance on greenhouse gas (GHG) emissions inventories, currently used by inventory compilers to fulfill national reporting requirements under the United Nations Framework Convention on Climate Change (UNFCCC) and new requirements of the Enhanced Transparency Framework (ETF) under the Paris Agreement starting in 2024. We review the relevant issues, including how air pollutant and GHG inventory activities have historically been structured, as well as potential benefits, challenges, and recommendations for coordinating GHG and air pollutant inventory efforts. We argue that while there are potential benefits to increasing coordination between air pollutant and GHG inventory development efforts, we also caution that there are differences in appropriate methodologies and applications that must jointly be considered.

REA: Resource Exergy Analysis - A basic guideline for application focusing on energy systems evaluation

This publication provides a basic guideline to the application of Resource Exergy Analysis (REA) with a focus on energy systems evaluation. REA is a proven application of exergy analysis to the field of technology comparison.REA aims to help decision makers to obtain an indicator in addition to GHG emissions, that is grounded in science, namely Resource Consumption.Even if an energy system uses GHG-free energy increased Resource Consumption likely increases the need for fossil fuels and thus GHG emissions of the global economy. Resource Consumption can replace the less comprehensive Primary Energy Consumption as an indictor and reduce the risk of suboptimal decisions.Evaluating energy systems using REA is key to ensure that climate targets are reached in time.

Multi-Objective Optimization of Hybrid Renewable Tri-Generation System Performance for Buildings

Hybrid renewable energy systems are subject to extensive research around the world and different designs have found their way to the market and have been commercialized. These systems usually employ multiple components, both renewable and conventional, combined in a way to increase the system’s overall efficiency and resilience and to lower GHG emissions. In this paper, a hybrid renewable energy system was designed for residential use and its annual energy performance was investigated and optimized. The multi-module hybrid system consists of a Ground-Air Heat Exchanger (GAHX), Photovoltaic Thermal (PVT) panels and Air to Water Heat Pump (AWHP). The developed system’s annual performance was simulated in the TRaNsient SYStem (TRNSYS) environment and optimized using the General Algebraic Modelling System (GAMS) platform. Multi-objective non-linear optimization algorithms were developed and applied to define optimal system design and performance parameters while reducing cost and GHG emissions. The results revealed that the designed system was able to satisfy building thermal heating/cooling loads throughout the year. The ground source heat exchanger contributed 21.3% and 26.3% of the energy during heating and cooling seasons, respectively. The initial design was optimized in terms of key performance parameters and module sizes. The annual simulation analysis showed that the system was able to self-generate and meet nearly 29.4% of the total HVAC electricity needs, with the rest being supplied by the grid. The annual system module performance efficiencies were 13.4% for the PVT electric and 5.5% for the PVT thermal, with an AWHP COP of 4.0.

Climate Change and Livestock Production: A Literature Review

Globally, the climate is changing, and this has implications for livestock. Climate affects livestock growth rates, milk and egg production, reproductive performance, morbidity, and mortality, along with feed supply. Simultaneously, livestock is a climate change driver, generating 14.5% of total anthropogenic Greenhouse Gas (GHG) emissions. Herein, we review the literature addressing climate change and livestock, covering impacts, emissions, adaptation possibilities, and mitigation strategies. While the existing literature principally focuses on ruminants, we extended the scope to include non-ruminants. We found that livestock are affected by climate change and do enhance climate change through emissions but that there are adaptation and mitigation actions that can limit the effects of climate change. We also suggest some research directions and especially find the need for work in developing country settings. In the context of climate change, adaptation measures are pivotal to sustaining the growing demand for livestock products, but often their relevance depends on local conditions. Furthermore, mitigation is key to limiting the future extent of climate change and there are a number of possible strategies.

Climate Change and Ocean Acidification: Are We Just Treating The Symptoms?

The human caused rise in atmospheric greenhouse gases has been seen as the driver of both climate change and ocean acidification. However recent peer reviewed papers show that, while GHG emissions are part of the problem, the primary driver of both climate change and ocean acidification is human caused ecological degradation. Curbing greenhouse gas emissions, to date, has been an abject failure but addressing ecological degradation within the remaining time frame is safe and achievable.

Machine Learning Approach to Simulate Soil CO2 Fluxes under Cropping Systems

With the growing number of datasets to describe greenhouse gas (GHG) emissions, there is an opportunity to develop novel predictive models that require neither the expense nor time required to make direct field measurements. This study evaluates the potential for machine learning (ML) approaches to predict soil GHG emissions without the biogeochemical expertise that is required to use many current models for simulating soil GHGs. There are ample data from field measurements now publicly available to test new modeling approaches. The objective of this paper was to develop and evaluate machine learning (ML) models using field data (soil temperature, soil moisture, soil classification, crop type, fertilization type, and air temperature) available in the Greenhouse gas Reduction through Agricultural Carbon Enhancement network (GRACEnet) database to simulate soil CO2 fluxes with different fertilization methods. Four machine learning algorithms—K nearest neighbor regression (KNN), support vector regression (SVR), random forest (RF) regression, and gradient boosted (GB) regression—were used to develop the models. The GB regression model outperformed all the other models on the training dataset with R2 = 0.88, MAE = 2177.89 g C ha−1 day−1, and RMSE 4405.43 g C ha−1 day−1. However, the RF and GB regression models both performed optimally on the unseen test dataset with R2 = 0.82. Machine learning tools were useful for developing predictors based on soil classification, soil temperature and air temperature when a large database like GRACEnet is available, but these were not highly predictive variables in correlation analysis. This study demonstrates the suitability of using tree-based ML algorithms for predictive modeling of CO2 fluxes, but no biogeochemical processes can be described with such models.

Variable Speed Diesel Generators: Performance and Characteristic Comparison

Diesel generators (DGs) are set to work as a backup during power outages or support the load in remote areas not connected to the national grid. These DGs are working at a constant speed to produce reliable AC power, while electrical energy demand fluctuates according to instantaneous needs. High electric loads occur only for a few hours a day in remote areas, resulting in oversizing DGs. During a low load operation, DGs face poor fuel efficiency and condensation of fuel residues on the walls of engine cylinders that increase friction and premature wear. One solution to increase combustion efficiency at low electric loads is to reduce diesel engine (DE) speed to its ideal regime according to the mechanical torque required by the electrical generator. Therefore, Variable Speed Diesel Generators (VSDGs) allow the operation of the diesel engine at an optimal speed according to the electrical load but require additional electrical equipment and control to maintain the power output to electrical standards. Variable speed technology has shown a significant reduction of up to 40% fuel consumption, resulting in low GHG emissions and operating costs compared to a conventional diesel generator. This technology also eliminates engine idle time during a low load regime to have a longer engine lifetime. The main objective of this survey paper is to present the state of the art of the VSDG technologies and compare their performance in terms of fuel savings, increased engine lifetime, and reduced greenhouse gases (GHG) emissions. Various concepts and the latest VSDG technologies have been evaluated in this paper based on their performance appraisal and degree of innovation.