• Energy Research
• 13. Climate action close
• 1. No poverty close
• 3. Good health close
• AU close
• PK close

In this work we assess the pathways for environmental improvement by the coal utilization industry for power generation in Australia. In terms of resources, our findings show that coal is a long term resource of concern as coal reserves are likely to last for the next 500 years or more. However, our analysis indicates that evaporation losses of water in power generation will approach 1000 Gl (gigalitres) per year, equivalent to a consumption of half of the Australian residential population. As Australia is the second driest continent on earth, water consumption by power generators is a resource of immediate concern with regards to sustainability. We also show that coal will continue to play a major role in energy generation in Australia and, hence, there is a need to employ new technologies that can minimize environmental impacts. The major technologies to reduce impacts to air, water and soils are addressed. Of major interest, there is a major potential for developing sequestration processes in Australia, in particular by enhanced coal bed methane (ECBM) recovery at the Bowen Basin, South Sydney Basin and Gunnedah Basin. Having said that, CO2 capture technologies require further development to support any sequestration processes in order to comply with the Kyoto Protocol. Current power generation cycles are thermodynamic limited, with 35-40% efficiencies. To move to a high efficiency cycle, it is required to change technologies of which integrated gasification combined cycle plus fuel cell is the most promising, with efficiencies expected to reach 60-65%. However, risks of moving towards an unproven technology means that power generators are likely to continue to use pulverized fuel technologies, aiming at incremental efficiency improvements (business as usual). As a big picture pathway, power generators are likely to play an increasing role in regional development; in particular EcoParks and reclaiming saline water for treatment as pressures to access fresh water supplies will significantly increase.

Understanding the relationship between climate and crop productivity is a key component of projections of future food production, and hence assessments of food security. Climate models and crop yield datasets have errors, but the effects of these errors on regional scale crop models is not well categorized and understood. In this study we compare the effect of synthetic errors in temperature and precipitation observations on the hindcast skill of a process-based crop model and a statistical crop model. We find that errors in temperature data have a significantly stronger influence on both models than errors in precipitation. We also identify key differences in the responses of these models to different types of input data error. Statistical and process-based model responses differ depending on whether synthetic errors are overestimates or underestimates. We also investigate the impact of crop yield calibration data on model skill for both models, using datasets of yield at three different spatial scales. Whilst important for both models, the statistical model is more strongly influenced by crop yield scale than the process-based crop model. However, our results question the value of high resolution yield data for improving the skill of crop models; we find a focus on accuracy to be more likely to be valuable. For both crop models, and for all three spatial scales of yield calibration data, we found that model skill is greatest where growing area is above 10-15 %. Thus information on area harvested would appear to be a priority for data collection efforts. These results are important for three reasons. First, understanding how different crop models rely on different characteristics of temperature, precipitation and crop yield data allows us to match the model type to the available data. Second, we can prioritize where improvements in climate and crop yield data should be directed. Third, as better climate and crop yield data becomes available, we can predict how crop model skill should improve.

Global mitigation pathways play a critical role in informing climate policies and targets that are in line with international climate goals. However, it is not possible to directly compare modelled results with national inventories used to assess progress under the UNFCCC due to differences in how land-based fluxes are accounted for.National inventories consider carbon flux on managed land using an area-based approach with managed land-areas determined by nations. Emissions scenarios consider a different managed land area and are calibrated against data from detailed global carbon cycle models that account for natural (indirect) and anthropogenic (direct) fluxes separately by design. To disentangle the direct and indirect components of land-based carbon fluxes, we use a reduced complexity climate model with explicit treatment of the land-use sector, OSCAR, one of the models used by the Global Carbon Project. We find the discrepancy between model and NGHGI-based accounting methods globally to be 4.4 ± 1.0 Gt CO2 yr-1 averaged over the 2000-2020 time period, which is in line with existing estimates. We then apply OSCAR to the set of pathways assessed by the IPCC to quantify how this gap evolves over time and estimate how key mitigation benchmarks change.Across both 1.5°C and 2°C scenarios, LULUCF emissions pathways aligned with NGHGI accounting practices show a strong increase in the total land sink until around mid-century. However, the ‘NGHGI alignment gap’  decreases over this period, converging in the 2050-2060s for 1.5°C scenarios and 2070s-2080s for 2°C scenarios. The convergence is primarily a result of the simulated stabilization and then decrease of the CO2-fertilization effect as well as background climate warming reducing the overall effectiveness of the land sink, which in turn reduces the indirect removals considered by NGHGIs. These dynamics lead to land-based emissions reversing their downward trend in most NGHGI-aligned scenarios by mid-century, and result in the LULUCF sector becoming a net-source of emissions by 2100 in about 25% of both 1.5°C and 2°C scenarios.Assessing emission pathways using LULUCF definitions from national inventory accounting results in downward revisions to emissions benchmarks derived from scenarios. NGHGI-aligned pathways result in earlier net-zero CO2 emissions by around 2-5 years for both 1.5°C and 2°C scenarios, and 2030 emission reductions relative to 2020 are enhanced by about 5 percentage points for both pathway categories. When incorporating the additional land removals considered by NGHGIs, the assessed cumulative net CO2 emissions to global net-zero CO2 also decreases systematically by 15-18% for both 1.5°C and 2°C scenarios.We find that increasing removals from direct fluxes in 1.5C scenarios overtake estimated removals using NGHGI conventions in the near term. However, by midcentury, the strengthening of direct removals is balanced by weakening of indirect removals, meaning that, on average, carbon removal on land accounted for using NGHGI conventions in 1.5C scenarios results in about half of the LULUCF removals in current policy scenarios. We discuss the implications of our results for future Global Stocktakes and market mechanisms under the Paris Agreement.

Abstract Energy poverty has evolved as an important issue of research from academic and policy perspectives during the last two decades. The energy poverty estimates exhibit widespread variations across regions, methodologies and datasets, making the task of energy and development policies challenging. The variations in energy poverty estimates necessitate a systematic and rigorous analysis of these empirical studies to obtain a true measure of energy poverty. The present study conducts a meta-analysis of 30 studies and 103 estimates of energy poverty. On average, 71.1 percent of the population in the overall sample studies has been identified as energy poor. High I-square statistic values suggest heterogeneity bias in the sample studies; therefore, analysis at the subgroup level has been conducted. Meta-regression analysis of overall sample and subgroups has identified type of methodology, citations, impact factor of the journal, regional dummies, average key crude oil price and gross domestic product as significant factors that explain heterogeneity in the energy poverty effect sizes. We recommend inclusion of local and regional factors in assessment of energy poverty for developing a uniform methodology to assess energy poverty across developing economies.

Estimation of methane adsorption capacity is crucial for the characterization of shale gas reservoirs. The methane adsorption capacity in shales is measured using high-pressure methane adsorption to obtain the adsorption isotherms, which can be fitted by Langmuir model. The determined Langmuir parameters can provide the methane adsorption capacity under actual reservoir conditions. In this study, a prediction model for the methane adsorption in shales was constructed based on 66 samples from 6 basins in China and Western Australia. The model was established in four steps: a model of Langmuir volume at experimental temperature, the temperature dependence of Langmuir volume, a model of Langmuir pressure, the temperature dependence of Langmuir pressure. In the model of Langmuir volume at experimental temperature, total organic carbon (TOC) and clay content (Vsh) were considered. A positive relationship was observed between the TOC and the temperature effect on the Langmuir volume. As the Langmuir pressure is sensitive to various factors, the Langmuir pressure at experimental temperature shows no trend with the TOC, clay content and thermal maturity, but a positive trend with the Langmuir volume. The results of this study can help log analysts to quantify adsorbed gas from well-log data since TOC and Vsh, which are the measure inputs of the introduced models, can be obtained from well-log data as well.

Abstract With emphasis on application in the built environment, this paper analyses meanings of the term ‘solar energy use’. It shows that failure to address the inherently vague and context dependent nature of the concept leads to much confusion, especially when attempts at absolute quantification are made. The paper argues that meaningful absolute quantification of solar energy use is impossible, and that misplaced attempts at this quantification have serious implications, especially in relation to decisions at the level of government policy. To avoid confusion, solar energy should not be considered as a component of conservation, energy-efficiency, or renewables, and definition and quantification of solar energy use should be qualified regarding purpose of quantification, type of energy, definition of use, baselines and context.

Lightning is a natural hazard that can have severe effects both on electrical power system and in general. In order to plan for lightning protection it is necessary to map out the likely occurrence of lightning at any location across the country. In this paper lightning density map of Pakistan has been developed using Pakistan Metrology Department (PMD) data over the period 1960–1990 on Arc-GIS software® platform. Such map is helpful in planning protection system for utility company's network and in general for lightning risk assessment at country level. Further lightning map also helps in predicting aviation hazards severe storm and in understanding the weather in any country. The developed lightning map shows that Pakistan lightning flash density is highest in region around Murree reaching a maximum of 9.4 flashes per km2 per year and most of the areas of Pakistan have lowest density reaching a minimum value of hardly single flash/km2/yr.

As an effective emission reduction approach, CO2 capture and storage (CCS) combined with enhanced water recovery (EWR) technology can not only reduce CO2 emissions, but can also recover deep saline water resources to relieve pressure on regional water resources, and can ensure the energy supply and both social and economic development. However, the environmental benefits and application costs of CCS-EWR are uncertain, and are determined by the technology level, geological conditions, and other physical factors. In this study, an optimal source-sink matching model and a techno-economic assessment model were developed to evaluate the contributions of CCS-EWR to carbon emission reduction and the increase of the water supply by considering various uncertain factors, as well as the corresponding costs. In addition, the Yellow River Basin (YRB) in China was selected as the research region because, while there are abundant coal-fired power plants (CFPPs) in the YRB, the water resources are scarce. The results revealed the following. (1) The maximum CO2 capture capacity of the 236 CFPPs in the YRB is about 738.77 Mt/a, and nearly 13.14 Gt of fresh water could be provided until the 236 CFPPs in the YRB retire, which can partially relieve the pressure on the supply of water resources. (2) With the consideration of the CCS-EWR benefits, the average cost of the 236 CFPPs in the YRB in their residual lifetime to reduce their CO2 emissions by 90% will be no more than 180 CNY/t. (3) The incentive effect of the increase of the industrial water price on the profits of CCS-EWR projects is not significant. CCS-EWR technology has better application prospects in China under the dual constraints of carbon-neutral targets and water shortages, and more policy support is required for its deployment.