Environmental effects of interstate power trading on electricity consumption mixes

Who shapes the embodied carbon dioxide emissions of interconnected power grids in China? A seasonal perspective

With the rapid development of interregional power transmission, the redistribution of fossil and renewable energy resources has changed sharply, and its complexity poses a challenge to the evaluation of power carbon emission responsibility. This study constructs an interprovincial power transmission framework to measure the seasonal carbon emissions embodied in regional electricity consumption over the period of 2008-2015 based on quarterly data. Then, a structural decomposition approach was developed to identify the influential factors of carbon emissions embodied in provincial electricity consumption from a seasonal perspective. The results show that the assessment for embodied emissions of power consumption based on different levels of data may vary by as much as 20%, and the carbon emissions and carbon intensity of power consumption exhibit significant seasonal characteristics. Furthermore, it is revealed that the economic scale in the fourth quarter makes the most significant contribution to the emissions increment, especially in underdeveloped provinces, while the change in energy efficiency of power generation reduces more carbon emissions in the first and second quarters. In addition, the impact of the power transmission scale is more significant in the third and fourth quarters, and it has been close to or even more than the impact of traditional factors in some quarters. Finally, the impact of economic scale, power generation energy intensity, power generation mix and electricity utilization efficiency on the emissions of regional power grids shows a relatively stable increasing trend, but this trend of directional stability is not reflected in the effect of the power transmission structure and transmission scale. This study contributes to the identification of the impact of the power transmission structure and transmission scale. Moreover, this study highlights the importance of considering seasonal characteristics when estimating the carbon emissions of power consumption and formulating specific emission reduction policies. Additionally, it provides a more accurate evaluation of carbon emissions and proposes several prominent recommendations for policy makers.

Water and Carbon Footprints of Electricity Are Sensitive to Geographical Attribution Methods

Environmental footprinting methods provide a means to relate the environmental externalities of electricity production to electricity consumers. Although several methods have been developed to connect the environmental footprint of electricity generation to end users, estimates produced by these methods are inherently uncertain due to the impossibility of actually tracing electricity from the point of generation to utilization. Previous studies rarely quantify this uncertainty, even though it may fundamentally alter their findings and recommendations. Here, we evaluate the sensitivity of water and carbon footprints estimates among seven commonly used methods to attribute electricity production to end users. We assess how sensitive water and carbon electricity footprint estimates are to attribution methods, how these estimates change over time, and the main factors contributing to the variability between methods. We evaluate and make available the water and carbon footprints of electricity consumption for every city across the contiguous United States for all assessed methods. We find significant but spatially heterogeneous variability in water and carbon footprint estimates across attribution methods. No method consistently overestimated or underestimated water and carbon footprints for every city. The variation between attribution methods suggests that future studies need to consider how the method selected to attribute environmental impacts through the electrical grid may affect their findings.

Tracking emissions in the US electricity system

Understanding electricity consumption and production patterns is a necessary first step toward reducing the health and climate impacts of associated emissions. In this work, the economic input-output model is adapted to track emissions flows through electric grids and quantify the pollution embodied in electricity production, exchanges, and, ultimately, consumption for the 66 continental US Balancing Authorities (BAs). The hourly and BA-level dataset we generate and release leverages multiple publicly available datasets for the year 2016. Our analysis demonstrates the importance of considering location and temporal effects as well as electricity exchanges in estimating emissions footprints. While increasing electricity exchanges makes the integration of renewable electricity easier, importing electricity may also run counter to climate-change goals, and citizens in regions exporting electricity from high-emission-generating sources bear a disproportionate air-pollution burden. For example, 40% of the carbon emissions related to electricity consumption in California's main BA were produced in a different region. From 30 to 50% of the sulfur dioxide and nitrogen oxides released in some of the coal-heavy Rocky Mountain regions were related to electricity produced that was then exported. Whether for policymakers designing energy efficiency and renewable programs, regulators enforcing emissions standards, or large electricity consumers greening their supply, greater resolution is needed for electric-sector emissions indices to evaluate progress against current and future goals.

Electricity Generation in LCA of Electric Vehicles: A Review

: Life Cycle assessments (LCAs) on electric mobility are providing a plethora of diverging results. 44 articles, published from 2008 to 2018 have been investigated in this review, in order to find the extent and the reason behind this deviation. The first hurdle can be found in the goal definition, followed by the modelling choice, as both are generally incomplete and inconsistent. These gaps influence the choices made in the Life Cycle Inventory (LCI) stage, particularly in regards to the selection of the electricity mix. A statistical regression is made with results available in the literature. It emerges that, despite the wide-ranging scopes and the numerous variables present in the assessments, the electricity mix’s carbon intensity can explain 70% of the variability of the results. This encourages a shared framework to drive practitioners in the execution of the assessment and policy makers in the interpretation of the results.

Virtual CO2 Emission Flows in the Global Electricity Trade Network

Quantifying greenhouse gas emissions due to electricity consumption is crucial for climate mitigation in the electric power sector. Current practices primarily use production-based emission factors to quantify emissions for electricity consumption, assuming production and consumption of electricity take place within the same region. The increasingly intensified cross-border electricity trade complicates the accounting for emissions of electricity consumption. This study employs a network approach to account for the flows in the whole electricity trade network to estimate CO₂ emissions of electricity consumption for 137 major countries/regions in 2014. Results show that in some countries, especially those in Europe and Southern Africa, the impacts of electricity trade on the estimation of emission factors and embodied emissions are significant. The changes made to emission factors by considering intergrid electricity trade can have significant implications for emission accounting and climate mitigation when multiplied by total electricity consumption of the corresponding countries/regions.

Marginal Emissions Factors for Electricity Generation in the Midcontinent ISO

Environmental consequences of electricity generation are often determined using average emission factors. However, as different interventions are incrementally pursued in electricity systems, the resulting marginal change in emissions may differ from what one would predict based on system-average conditions. Here, we estimate average emission factors and marginal emission factors for CO₂, SO₂, and NO_x from fossil and nonfossil generators in the Midcontinent Independent System Operator (MISO) region during years 2007-2016. We analyze multiple spatial scales (all MISO; each of the 11 MISO states; each utility; each generator) and use MISO data to characterize differences between the two emission factors (average; marginal). We also explore temporal trends in emissions factors by hour, day, month, and year, as well as the differences that arise from including only fossil generators versus total generation. We find, for example, that marginal emission factors are generally higher during late-night and early morning compared to afternoons. Overall, in MISO, average emission factors are generally higher than marginal estimates (typical difference: ∼20%). This means that the true environmental benefit of an energy efficiency program may be ∼20% smaller than anticipated if one were to use average emissions factors. Our analysis can usefully be extended to other regions to support effective near-term technical, policy and investment decisions based on marginal rather than only average emission factors.

CO2 Emissions Embodied in Interprovincial Electricity Transmissions in China

Existing studies on the evaluation of CO₂ emissions due to electricity consumption in China are inaccurate and incomplete. This study uses a network approach to calculate CO₂ emissions of purchased electricity in Chinese provinces. The CO₂ emission factors of purchased electricity range from 265 g/kWh in Sichuan to 947 g/kWh in Inner Mongolia. We find that emission factors of purchased electricity in many provinces are quite different from the emission factors of electricity generation. This indicates the importance of the network approach in accurately reflecting embodied emissions. We also observe substantial variations of emissions factors of purchased electricity within subnational grids: the provincial emission factors deviate from the corresponding subnational-grid averages from -58% to 44%. This implies that using subnational-grid averages as required by Chinese government agencies can be quite inaccurate for reporting indirect CO₂ emissions of enterprises' purchased electricity. The network approach can improve the accuracy of the quantification of embodied emissions in purchased electricity and emission flows embodied in electricity transmission.

Comparative Assessment of Models and Methods To Calculate Grid Electricity Emissions

Due to the complexity of power systems, tracking emissions attributable to a specific electrical load is a daunting challenge but essential for many environmental impact studies. Currently, no consensus exists on appropriate methods for quantifying emissions from particular electricity loads. This paper reviews a wide range of the existing methods, detailing their functionality, tractability, and appropriate use. We identified and reviewed 32 methods and models and classified them into two distinct categories: empirical data and relationship models and power system optimization models. To illustrate the impact of method selection, we calculate the CO2 combustion emissions factors associated with electric-vehicle charging using 10 methods at nine charging station locations around the United States. Across the methods, we found an up to 68% difference from the mean CO2 emissions factor for a given charging site among both marginal and average emissions factors and up to a 63% difference from the average across average emissions factors. Our results underscore the importance of method selection and the need for a consensus on approaches appropriate for particular loads and research questions being addressed in order to achieve results that are more consistent across studies and allow for soundly supported policy decisions. The paper addresses this issue by offering a set of recommendations for determining an appropriate model type on the basis of the load characteristics and study objectives.

Energy and Emissions from U.S. Population Shifts and Implications for Regional GHG Mitigation Planning

Living in different areas is associated with different impacts; the movement of people to and from those areas will affect energy use and emissions over the U.S. The emissions implications of state-to-state migration on household energy and GHG emissions are explored. Three million households move across state lines annually, and generally move from the North East to the South and West. Migrating households often move to states with different climates (thus different heating and cooling and needs), different fuel mixes, and different regional electricity grids, which leads them to experience changes in household emissions as a result of their move. Under current migration trends, the emissions increases of households moving from the Northeast to the South and Southwest are balanced by the emissions decreases of households moving to California and the Pacific Northwest. The net sum of emissions changes for migrating households is slightly positive but near zero; however, that net zero sum represents the balance of many emission changes. Planning for continued low carbon growth in low carbon regions or cities experiencing high growth rates driven by migration is essential in order to offset the moderate emissions increases experienced by households moving to high carbon regions.

Inventory development and input-output model of U.S. land use: relating land in production to consumption

As populations and demands for land-intensive products, e.g., cattle and biofuels, increase the need to understand the relationship between land use and consumption grows. This paper develops a production-based inventory of land use (i.e., the land used to produce goods) in the U.S. With this inventory an input-output analysis is used to create a consumption-based inventory of land use. This allows for exploration of links between land used in production to the consumption of particular goods. For example, it is possible to estimate the amount of cropland embodied in processed foods or healthcare services. As would be expected, agricultural and forestry industries are the largest users of land in the production-based inventory. Similarly, we find that processed foods and forest products are the largest users of land in the consumption-based inventory. Somewhat less expectedly this work finds that the majority of manufacturing and service industries, not typically associated with land use, require substantial amounts of land to produce output due to the purchase of food and other agricultural and wood-based products in the supply chain. The quantitative land use results of this analysis could be integrated with qualitative metrics such as weighting schemes designed to reflect environmental impact or life cycle impact assessment methods.

Life cycle assessment and grid electricity: what do we know and what can we know?

The generation and distribution of electricity comprises nearly 40% of U.S. CO(2), emissions, as well as large shares of SO(2), NO(x), small particulates, and other toxins. Thus, correctly accounting for these electricity-related environmental releases is of great importance in life cycle assessment of products and processes. Unfortunately, there is no agreed-upon protocol for accounting for the environmental emissions associated with electricity, as well as significant uncertainty in the estimates. Here, we explore the limits of current knowledge about grid electricity in LCA and carbon footprinting for the U.S. electrical grid, and show that differences in standards, protocols, and reporting organizations can lead to important differences in estimates of CO(2) SO(2), and NO(x) emissions factors. We find a considerable divergence in published values for grid emissions factor in the U.S. We discuss the implications of this divergence and list recommendations for a standardized approach to accounting for air pollution emissions in life cycle assessment and policy analyses in a world with incomplete and uncertain information.