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Society’s efforts to ‘manage’ the problem of human-induced climate change – for example through setting targets, tracking progress, imposing sanctions and incentives, and creating markets in emission rights and offsets – have given rise to numerous calculation, measurement, attribution, monitoring, reporting and verification challenges, which are being addressed by many different communities (including scientists, governments, businesses and accountants) in many different ways. Carbon accounting – this diverse and ever-expanding assemblage of calculative practices – is a rapidly evolving phenomenon, which has only recently become a subject of academic accountancy-related research. This thesis explores what carbon accounting means, who it involves, and how different communities define and lay claim to competence in the field. It also examines, through case studies on the emergence of the Climate Disclosure Standards Board and the controversies around generating tradable carbon offsets from forestry projects in the UK, the immense technical, cognitive, social and political work required to make carbon measurable, commensurable and thereby amenable to various forms of management. The thesis contributes to both conceptual and practical understanding of carbon accounting as an emerging field of study. Bringing together a wide range of empirical examples of different types of carbon accounting practices, it proposes a unique definition of carbon accounting which expands the horizons of the field. It provides a conceptual basis for making sense of carbon accounting by considering it not as a unitary phenomenon but rather as a set of overlapping frames, each associated with different communities of practice. It shows that competence in carbon accounting is contested, particularly where these frames overlap, and that boundary organisations are emerging that offer the opportunity to negotiate such tensions and lead to more productive policy-making. Finally, it makes the case that engagement with the detail of the ‘nuts and bolts’ of carbon accounting is essential, as these apparently technical details can have major implications for the effectiveness of society’s response to climate change, and it is only by opening them up to rigorous scrutiny that we can make progress, both conceptually and practically.

The transportation sector accounts for 40% of California’s CO2e emissions. Light electric vehicles (EVs) have been identified as a lynch pin for decarbonization. EV emissions are dependent on the energy mix of the electric grid and when the vehicles are charged. Calculating emissions benefits are dependent on the counterfactual emissions being displaced Assuming a direct replacement of ICE emissions at a constant rate does not consider the emissions benefits of charging at different times of day or locations. The study evaluates the historic emissions benefits of three California investor-owned utility (IOU) EV charging infrastructure programs from 2017 to early-2023 and develops a model to forecast future emissions impacts for EV charging infrastructure installed in each utility’s respective service territory. For historic emissions analysis, the study employs an EV charging infrastructure demand forecast model to accurately evaluate the emissions benefit of EV charging infrastructure using emission scenario counterfactuals. For emissions forecast, the study uses a novel emissions allocation framework to more finely account for commuting behaviors and the impact of charging behavior depending on availability of workplace or at-home charging. The study found the three programs saved 12,000 mt CO2e (3 mt CO2e per port) driven by workplace charging, which delivered greater emissions savings compared to residential charging. Workplace charging was found to be the most efficient charging type based on grams (g) of CO2e saved per dollar invested but increasing workplace charging deployment delivered diminishing emissions returns. Behind-the-meter (BTM) costs were the primary driver for project costs. Improved program design to reduce those costs can decrease total deployment costs and deliver more emissions per dollar spent.

This project was conducted by six University of Michigan’s School of Environment and Sustainability graduate students (referred to as the research team) to support Meijer’s core values of sustainability by developing a Scope 3 greenhouse gas emissions (GHG) accounting framework and conducting a baseline emissions inventory for the fiscal year of 2021. Meijer has made significant progress in Scope 1 and Scope 2 goal setting and reductions. This research document outlines the foundational framework Meijer will use to carry out its Scope 3 GHG emissions inventory and calculates a benchmark inventory to inform goal-setting in the future. As a retailer with business activities including procurement, distribution, manufacturing, and retail operations in six U.S. states, Meijer has a vast and complex value chain. In order to expand its sustainability programs and goals outside of its direct operations and into its value chain, Meijer must first complete a Scope 3 inventory to identify the most pertinent areas of impact and the most carbon intensive categories. This report helps establish that baseline. Scope 3 emissions are defined as “indirect emissions that occur in a company’s value chain.” These are separate from the Scope 1 and 2 emissions that occur as part of a company’s direct operations. Scope 3 is intended to record, measure and manage the emissions occurring in a company’s value chain, between a company’s suppliers, manufacturers, and customers. The categories are intended to be mutually exclusive to avoid double counting emissions across Scope 3 categories or with Scope 1 or 2 GHG accounting. The research team utilized the The Corporate Value Chain (Scope 3) Accounting and Reporting Standard as guidance for calculating the relevant and material Scope 3 categories for Meijer. Out of the 15 Scope 3 Categories, 11 were deemed material; the four eliminated categories were excluded based on insignificant materiality to the business. The research team worked with internal Meijer stakeholders to procure data in alignment with the GHG Protocol guidance. Calculation methods were selected given data availability and constraints. A major challenge the research team encountered when mapping the US Environmentally-Extended Input-Output (USEEIO) emission factors, specifically for Category 1, to Meijer’s categories of goods and services, were misaligned categories between the two. To identify the business area in both sales and spend data, Meijer uses UNSPSC ID codes. It is difficult to map these codes to the EPA’s Emission Factors (EFs), which use NAICS codes, as there are not always direct matches. Additionally, the USEEIO methodology is based on an estimate of economic value of the goods and services, so as Meijer’s sales grow in the future, so will Category 1 while using the USEEIO methodology. However, the USEEIO methodology is well suited to illustrate emission “hotspots'' and which categories are the highest carbon emitters within Category 1. Going forward, Meijer could use its supplier’s Scope 1 and Scope 2 emissions data to calculate Meijer’s Scope 3 Purchased Goods and Services, eliminating the need for USEEIO methodology. 1 As part of this research report, the team inventoried Meijer’s emissions for the baseline year of 2021. The results are as follows: The total estimated Scope 3 emissions is 19,200,000 Mt CO2e. The three most contributing categories are Purchased Goods (65% of total emissions), Use of Sold Products (24% of total emissions), and Downstream Transportation and Distribution (5% of total emissions) as shown in ES 1. For additional reference, Meijer’s Scope 1 total was 271,770 Mt CO2e and Scope 2 total was 666,417 Mt CO2e (for a combined total of 938,187 Mt CO2e) for Fiscal Year (FY) 2021.

This report presents research conducted in conjunction with Coopedota R.L., a coffee producing co-op based out of Santa Maria de Dota, Costa Rica. Within Coopedota’s nearly 900 participating co-op members, this research focuses on the 69 Rainforest Alliance (RA) certified co-op farmers and their production area. The overarching goal of this work is to enhance the net-zero efforts of Coopedota by gaining a deeper understanding of the current carbon stored in their production area and making recommendations for future use of their entire production systems. The team approached the goal through three primary lenses: understanding farmers' perspectives on organic practices, calculating the carbon storage within the RA farmer production area, and evaluating alternatives to coffee cherry’s end of life. To achieve this goal, the team: 1) carried out in-depth interviews with 34 RA famers concerning their thoughts on different certification processes and sustainable farming practices, 2) created 42 permanent plots within RA farmers’ production area, collecting data to create a carbon inventory of the shade biomass, 3) created a life cycle assessment (LCA) of coffee production including gasification and biochar as an end of life alternative to composting for the treatment of coffee pulp. Supporting the first objective, the research team interviewed 34 RA farmers on their sustainability and farming practices, opinions on the new Rainforest Alliance requirements, and the possibility of Coopedota starting an in-house certification process. Results found that RA farmers' main concerns regarding certification processes are having strong enough financial incentives and allowing minor use of chemicals when necessary. Supporting the second objective, the team collected carbon storage data from 34 RA farmers' production areas and 8 farms within Coopedota’s private production area. Each plot was 34 x 34 m and collectively represented 1% of all RA farmers’ production area. On average, each plot contained 194.35 tonnes of CO2e. Total CO2e in the 42 plots was 8162.94 tonnes of CO2e, which was used to estimate the approximate CO2e in all RA farmers’ production areas to be 816,293.50 tonnes. The last objective required the creation of a simplified LCA, which involved coffee cultivation and production of greenhouse gas emissions following technical and agricultural recommendations to reduce greenhouse gas emissions. Upon review of the LCA, there is potential for carbon negative results as the process of gasification can be used to replace Coopedota’s electrical needs and eliminate methane emissions produced during the end-of-life phase of the coffee production process. Gasification produces biochar as a byproduct, which is a substance that can be added to fertilizer as an enhancement and further aid farmers in the production of sustainable coffee with substantial growth.

As the momentum of climate action continues to shift from top-down, nation-state action to decentralized action by corporations, cities, and utilities, these actors are working to decarbonize their energy consumption and help drive the transition to a carbon-free grid. However, the current frameworks and metrics that drive voluntary decision making—GHG inventories and renewable energy procurement goals—do not always reflect the reality of the evolving power system, and thus can lead to decisions that do not effectively reduce emissions or address grid needs. This research seeks to fill the gaps in knowledge about how both regulatory and voluntary approaches to decarbonizing the electric grid can maximize their effectiveness and accurately measure and attribute emissions from electricity consumption. This dissertation draws upon power system engineering and industrial ecology research and applies data science and optimization methods to 1) identify whether there is a need to account for grid carbon emissions on an hourly basis, 2) introduce a comprehensive dataset of validated hourly emissions from the U.S. power sector, and 3) introduce a new modeling tool that enables greater understanding of the role of different voluntary clean energy procurement goals in the broader energy transition.

Climate change poses an unprecedented threat that will require a systemic transition away from high-carbon activities to avoid the worst effects (IPCC, 2015). Bank lending makes up a significant source of external capital for businesses, yet clear market and policy incentives to accelerate the needed shift of investment to low-carbon assets is lacking (Campiglio, 2016). Further, due to the difficulty of calculating the greenhouse gas (GHG) emissions related to the clients and economic activities they finance, banks cannot currently set appropriate emissions reduction targets (Science Based Targets Initiative, 2017). This is a result of low data availability and no market standard for measuring portfolio climate impact. The most common metric, ‘financed emissions’, the emissions attributed to a bank or investor financing an emitting company, has not yet proven itself accurate enough to be widely adopted among banks for target-setting. Meanwhile a new metric, ‘financed technology’, the analysis of the type and quantity of a GHG-emitting or -reducing technology or asset directly or indirectly financed in a given portfolio, has recently emerged as a possible alternative metric but has yet to be widely tested. Management begins with measurement, and without reliable, proven metrics, banks will be ill-equipped to set appropriate targets. Previous studies have qualitatively reviewed existing methodologies. To date, however, there is no guidance on which metrics and methods outperform others in terms of accuracy and utility for effective target-setting. Furthermore, no research has quantitatively compared the two above-mentioned metrics. This thesis, therefore, quantitatively analyzes and compares the ‘financed emissions’ and ‘financed technology’ metrics and underlying methodologies to determine whether or not these are accurate and useful for target-setting. In order to do so, this thesis presents a new framework to compare data inputs – the Data Input Accuracy Scoring (DIAS) framework. For further determining the usefulness of each metric for target-setting, a qualitative review, including a case study, was applied. My first hypothesis was that due to the unavailability of data for the majority of a bank’s clients representing the largest concentration of emissions, methodologies utilizing GHG emissions as a metric will not be an accurate means for a bank to measure and steer its portfolio. I also hypothesized that sector-based metrics focused on financed-technology yield more accurate results that can be utilized for target-setting. While several financed emissions methods exist, one in particular has emerged among some European banks: the so-called ‘PCAF’ (Platform Carbon Accounting Financials) approach. With regards to ‘financed technology’ methods, one has recently been developed and is rapidly garnering attention, the so-called ‘PACTA’ (Paris Alignment Capital Transition Assessment) approach. In order to quantitatively compare these two methodologies, the present research utilizes a contrived lending portfolio as a control variable by which the metrics were tested and compared on the basis of accuracy and utility for target-setting. Next to contributing a framework by which climate metrics can be objectively assessed, this research identifies key elements for setting targets in line with the below- 2˚C target, providing a clear, quantitative distinction between methodologies. In line with my hypotheses, the two most material findings are 1) that the data inputs of the financed emissions approach suffer considerable inaccuracies, yielding a much lower accuracy score than the financed technology inputs and 2) that target-setting requires a sector-based approach. The results of the present research can help inform banks regarding which metrics show the most promise for target-setting. Furthermore, the findings could contribute to the establishment of a market standard that policy makers and stakeholders can hold banks accountable to. This in-turn should help incentivize the accelerated shift of finance from high- to low-carbon assets.

Cities and urban areas are likely to be the important foci for greenhouse gas (GHG) mitigation efforts at the global scale. The Intergovernmental Panel on Climate Change report pointed out that approximately 71% of global energy-related GHG are associated with urban areas, which house about 54% of world population. The proportion of anthropogenic GHG associated with urban areas and cities is expected to increase in the future, when urban areas are expected to accommodate approximately 66% of world population by 2050 as predicted by the United Nations. Infrastructure which includes the provisioning of energy, transportation, water, municipal waste management, and building material plays a big role in urban energy use and GHG emissions. Studies in my dissertation investigated low-carbon cities through an interdisciplinary focus on infrastructure systems. Four chapters in my dissertations have made contributions 1) to evaluate carbon impacts of urban infrastructure at the city level to provide customized mitigation actions and scaling-up cities’ carbon impact aggregately to national level; 2) to identify a new and unique cross-sectoral infrastructure action and quantify its carbon mitigation potential; and 3) to investigate infrastructure operators’ motivation for low-carbon transitions and the enabling factors for the transitions from co-evolutionary perspectives. Details of each study are presented below. Quantify carbon impacts of urban infrastructure at the city level to provide customized mitigation actions (Chapter 2): With massive urbanization and infrastructure investments occurring in China, understanding GHG emissions from infrastructure use in small and large Chinese cities with different administrative levels is important for building future low-carbon cities. This paper identifies diverse data sources to assess greenhouse gas (GHG) emissions based on the community-wide infrastructure footprints (CIF) method in four Chinese cities of varying population (1 to 20 million people) and administrative levels: Yixing, Qinhuangdao, Xiamen and Beijing. CIF quantifies GHGs associated with seven infrastructure sectors providing energy (fuels/coal), electricity, water supply and wastewater treatment, transportation, municipal waste management, construction materials, and food to support urban activities. Industrial energy use dominates the infrastructure CIFGHG in all four cities, ranging from 76% of total CIF in Yixing to 30% in Beijing, followed by residential energy use (6-13%), transportation (4-12%), commercial energy use (2-25%), food (6-11%), cement use (3-8%) and water (about 1%), thereby identifying priorities for low-carbon infrastructure development. Transboundary footprint contributions ranged from 31% (Beijing) to 8% (Qinhuangdao), indicating that infrastructure supply chains of cities are important. GHGs from energy use are dominated by electricity (35-45%) and non-electricity coal use (30-50%). The authors demonstrated that disaggregated infrastructure use-efficiency metrics in each infrastructure sector provide useful baseline performance data for comparing different cities. Database development for scaling-up all cities’ carbon impact aggregately to the national level in China (Chapter 3): Many studies have quantified carbon emissions from a few cities in a nation, while few studies have estimated emissions from all cities in a nation to assess their collective contributions towards national total. This paper, focusing on Chinese cities, assesses the collective contribution of all cities to national carbon emissions, the share of carbon emissions by city types, and carbon emission per capita and per GDP. This paper describes the Chinese City Industrial-Infrastructure database including fuel/electricity use and heat supply in 644 cities, in which energy use is aligned with national data with ~1% difference. It is found that direct carbon emissions from 644 Chinese cities collectively contribute to 62.4% of the national CO2 emissions. Further categorizing these cities based on population size, economic structure, and administrative level, it is found that Midsize cities (0.5-3 million) accounted for 38.1% of national CO2 emissions; Mixed-Economy cities contributed to about 40% of the national CO2 emissions; and city propers (all urban administrative districts in a city) collectively contribute to 42.9% of the national CO2 emissions. Direct emissions per capita ranged from 0.94 to 83.3 tonnes CO2 per person (8.85 tonnes/person on average). Direct emissions per GDP ranged from 0.01 to 2.60 kg CO2 per yuan-GDP (0.26 kg CO2/yuan-GDP on average). Direct plus embedded emissions in electricity were also evaluated and found to have similar patterns as direct carbon emissions. These results enhance our understanding of the share of carbon emissions from Chinese cities and suggest the importance of focusing on certain city types for mitigation efforts. Identify a new and unique cross-sectoral infrastructure action and quantify its carbon mitigation potential in Chinese cities (Chapter 4): Utilizing low-grade waste heat from industries to heat and cool homes and businesses through the 4th generation district energy systems (DES) is a novel strategy to reduce energy use. This paper develops a generalizable methodology to estimate the energy saving potential for heating/cooling in 20 cities in two Chinese provinces, representing cold winter and hot summer regions respectively. We also conduct a life-cycle analysis of the new infrastructure required for energy exchange in DES. Results show that heating and cooling energy use reduction from this waste heat exchange strategy varies widely based on the mix of industrial, residential and commercial activities, and climate conditions in cities. Low-grade heat is found to be the dominant component of waste heat released by industries, which can be reused for both district heating and cooling in the 4th generation DES, yielding energy use reductions from 12% to 91% (average of 58%) for heating and 12% to 100% (average of 73%) for cooling energy use in different cities based on annual exchange potential. Incorporating seasonality and multiple energy exchange pathways resulted in energy savings reductions from 0 to 87%. The life-cycle impact of added infrastructure was small (

Despite the availability of cost-effective alternatives to highly carbon-intensive practices, the world continues to invest in fossil fuel energy systems. For universities that have pledged to become carbon neutral, this concept of carbon lock-in raises the stakes of their carbon commitments, presenting challenges to traditional practices in facilities planning and operations. Building upon past research on carbon lock-in effects on college campuses, this thesis seeks to identify the University of Dayton’s over-committed emissions under a business-as-usual scenario and chart out a course for decarbonization pathways that would unlock those emissions that are hardest to avoid. I find the business-as-usual scenario results in high carbon liability at the neutrality date, which represents high costs to offset carbon emissions or purchase other “end-of-pipe” solutions. I also discuss decarbonization pathways that could unlock these over-committed emissions. Future work should explore some of the carbon unlocking strategies discussed here so the university can begin to integrate them into its climate action plan and construction policies. Additionally, this perspective on carbon lock-in will be useful to administrators and facilities managers at other institutions concerned about carbon neutrality and high carbon liabilities associated with existing and future carbon-emitting infrastructure.

Quantifying terrestrial carbon (C) stocks in vineyards represents an important opportunity for estimating C sequestration in perennial cropping systems. Considering ~230,000 ha in California (8.2% of total land cultivated in CA) are dedicated to wine grape production, annual C capture and storage in woody biomass is substantial. In this study, destructive sampling was used to measure C stocks in the woody biomass of a 15-year-old Cabernet Sauvignon vines from a California Northern San Joaquin Valley vineyard. The objectives were to characterize C stocks in terms of allometric variation between biomass fractions of roots, aboveground wood, canes, leaves and fruit, and then test correlations between easy-to-measure variables such as trunk diameter, pruning weights and harvest index to vine biomass fractions. Carbon stocks were also estimated from the volume of biomass in mounds generated during vineyard removal, and compared with previous estimates of standing biomass.Total vine C was estimated at 12.3 Mg C/ha, of which 8.9 Mg C/ha came from perennial vine biomass, whereas annual biomass was estimated at 1.7 Mg C/ha from leaves and canes and 1.7 Mg C/ha from fruit. High positive correlations were found between the diameter of the trunk and overall woody C stocks (r2 = 0.84), pruning weights and annual C stocks (r2 = 0.93), and between fruit weight and annual C stocks (r2 = 0.95). Carbon estimates in the mounds of vine biomass fractions wood, root and canes (10.25 Mg C/ha) were not significantly different than for individually measured vines (10.02 Mg C/ha).This research demonstrates that allometric equations represent strong predictive power for C estimations due to high correlations and low error between these simple measurements and C stocks. Such equations, using information collected from vineyard management practices, could enable growers to estimate C stocks more easily and would facilitate managing C at a vineyard level. This might also provide the basis to calculate future C stock estimations, especially important considering the significance that C sequestration is taking in the production function of agro-ecosystems.