Abel Chavez

Two Approaches to Greenhouse Gas Emissions Foot-Printing at the City Scale

C greenhouse gas (GHG) accounting is confounded by the relatively small spatial size of cities compared to nations, due to which • Essential infrastructures—commuter and airline transport, energy supply, water supply, wastewater infrastructures, etc.—cross city boundaries, hence energy use to provide these services often occurs outside the boundary of the cities using them. • Significant trade of other goods and services also occurs across cities, with associated embodied GHGs. Consequently, human activity in cities—occurring in residential, commercial, and industrial sectors—stimulates in-boundary GHG emissions occurring within the geopolitical boundary of the community, as well as trans-boundary emissions (occurring outside). Allocating in-boundary and trans-boundaryGHG emissions to communities can be achieved using different approaches described below.

Progress toward low carbon cities: approaches for transboundary GHG emissions’ footprinting

Cities are home to a large proportion of the world’s population and as a result, are being recognized as major contributors to global GHG emissions. There is a need to establish baseline GHG emission accounting protocols that provide consistent, reproducible, comparable and holistic GHG accounts that incorporate in-boundary and transboundary GHG impacts of urban activities and support policy intervention. This article provides a synthesis of previously published GHG accounts for cities by organizing them according to their in-boundary and transboundary considerations, and reviewing three broad approaches that are emerging for city-scale GHG emissions accounting: geographic accounting, transboundary infrastructure supply chain (TBIS) footprinting, and consumption-based footprinting. The TBIS and consumption-based footprints are two different approaches that result in different estimates of a community’s GHG emissions, and inform policies differently, as illustrated with a case study of Denver, CO, USA. The conceptual discussions around TBIS and consumption-based footprints indicate that one single metric (e.g., GHG/person) will probably not be suitable to represent GHG emissions associated with cities, and it will take a combination of variables for defining a low-carbon city.

Implementing Trans‐Boundary Infrastructure‐Based Greenhouse Gas Accounting for Delhi, India

Community‐wide greenhouse gas (GHG) emissions accounting is confounded by the relatively small spatial size of cities compared to nations—due to which, energy use in essential infrastructures serving cities, such as commuter and airline transport, energy supply, water supply, wastewater infrastructures, and others, often occurs outside the boundaries of the cities using them. The trans‐boundary infrastructure supply chain footprint (TBIF) GHG emissions accounting method, tested in eight U.S. cities, incorporates supply chain aspects of these trans‐boundary infrastructures serving cities, and is akin to an expanded geographic GHG emissions inventory. This article shows the results from applying the TBIF method in the rapidly developing city of Delhi, India. The objectives of this research are to (1) describe the data availability for implementing the TBIF method within a rapidly industrializing country, using the case of Delhi, India; (2) identify methodological differences in implementation of the TBIF method between Indian versus U.S. cities; and (3) compare broad energy use metrics between Delhi and U.S. cities, demonstrated by Denver, Colorado, USA, whose energy use characteristics and TBIF GHG emissions have previously been shown to be similar to U.S. per capita averages. This article concludes that most data required to implement the TBIF method in Delhi are readily available, and the methodology could be translated from U.S. to Indian cities. Delhis 2009 community‐wide GHG emissions totaled 40.3 million metric tonnes of carbon dioxide equivalents (t CO‐eq), which are normalized to yield 2.3 t CO‐eq per capita; nationally, India reports its average per capita GHG emissions at 1.5 t CO‐eq. In‐boundary GHG emissions contributed to 68% of Delhis total, where end use (including electricity) energy in residential buildings, commercial and industrial usage, and fuel used in surface transportation contributed 24%, 19%, and 21%, respectively. The remaining 4% of the in‐boundary GHG emissions were from waste disposal, water and wastewater treatment, and cattle. Trans‐boundary infrastructures were estimated to equal 32% of Delhis TBIF GHG emissions, with 5% attributed to fuel processing, 3% to air travel, 10% to cement, and 14% to food production outside the city.

What metrics best reflect the energy and carbon intensity of cities? Insights from theory and modeling of 20 US cities

Three broad approaches have emerged for energy and greenhouse gas (GHG) accounting for individual cities: (a) purely in-boundary source-based accounting (IB); (b) community-wide infrastructure GHG emissions footprinting (CIF) incorporating life cycle GHGs (in-boundary plus trans-boundary) of key infrastructures providing water, energy, food, shelter, mobility–connectivity, waste management/sanitation and public amenities to support community-wide activities in cities—all resident, visitor, commercial and industrial activities; and (c) consumption-based GHG emissions footprints (CBF) incorporating life cycle GHGs associated with activities of a sub-set of the community—its final consumption sector dominated by resident households. The latter two activity-based accounts are recommended in recent GHG reporting standards, to provide production-dominated and consumption perspectives of cities, respectively. Little is known, however, on how to normalize and report the different GHG numbers that arise for the same city. We propose that CIF and IB, since they incorporate production, are best reported per unit GDP, while CBF is best reported per capita. Analysis of input–output models of 20 US cities shows that GHGCIF/GDP is well suited to represent differences in urban energy intensity features across cities, while GHGCBF/capita best represents variation in expenditures across cities. These results advance our understanding of the methods and metrics used to represent the energy and GHG performance of cities.

Quantifying carbon mitigation wedges in U.S. cities: near-term strategy analysis and critical review.

A case study of Denver, Colorado explores the roles of three social actors-individual users, infrastructure designer-operators, and policy actors-in near-term greenhouse gas (GHG) mitigation in U.S. cities. Energy efficiency, renewable energy, urban design, price- and behavioral-feedback strategies are evaluated across buildings-facilities, transportation, and materials/waste sectors in cities, comparing voluntary versus regulatory action configurations. GHG mitigation impact depends upon strategy effectiveness per unit, as well as societal participation rates in various action-configurations. Greatest impact occurs with regulations addressing the vast existing buildings stock in cities, followed by voluntary behavior change in electricity use/purchases, technology shifts (e.g., to teleconferencing), and green-energy purchases among individual users. A portfolio mix of voluntary and regulatory actions can yield a best-case maximum of ~1% GHG mitigation annually in buildings and transportation sectors, combined. Relying solely on voluntary actions reduces mitigation rates more than five-fold. A portfolio analysis of climate action plans in 55 U.S. cities reveals predominance of voluntary outreach programs that have low societal participation rates and hence low GHG impact, while innovative higher-impact behavioral, technological, and policy/regulatory strategies are under-utilized. Less than half the cities capitalize on cross-scale linkages with higher-impact state-scale policies. Interdisciplinary field research can help address the mis-match in plans, actions, and outcomes.

Planning for low-carbon communities in US cities: a participatory process model between academic institutions, local governments and communities in Colorado

Participatory process models combine the use of technical data with community participation to develop a sustainability plan relevant to each city. In this article, two case study applications in Denver, CO, USA and Broomfield, CO, USA use a participatory process, which combines teams from academia, local governments and community members to create city climate action plans. The participatory process is developed from concepts in community-based participatory research, analytic deliberation, and post-normal science. The refined process model developed in these two case studies goes through seven steps which include creating the deliberative body, co-developing data sets for sustainability analysis, defining sustainability goals, using scenario modeling for potential sustainability actions, prioritizing actions through deliberation, demonstrating consensus or diversity in final action plan, and conducting an outcomes assessment.

Increasing Precision in Greenhouse Gas Accounting Using Real‐Time Emission Factors

For many companies, the greenhouse gas (GHG) emissions associated with their purchased and consumed electricity form one of the largest contributions to the GHG emissions that result from their activities. Currently, hourly variations in electricity grid emissions are not considered by standard GHG accounting protocols, which apply a national grid emission factor (EF), potentially resulting in erred estimates for the GHG emissions. In this study, a method is developed that calculates GHG emissions based on real‐time data, and it is shown that the use of hourly electricity grid EFs can significantly improve the accuracy of the GHG emissions that are attributed to the purchased and consumed electricity of a company. A model analysis for the electricity delivered to the Spanish grid in 2012 reveals that, for companies operating during the day, GHG emissions calculated by the real‐time method are estimated to be up to 5% higher (and in some special cases up to 9% higher) than the emissions calculated by the conventional method in which a national grid EF is applied, whereas for companies operating during nightly hours, GHG emissions are estimated to be as low as 3% below the GHG emissions determined by the conventional method. A significant error can therefore occur in the organizational carbon footprint (CF) of a company and, consequently, also in the product CF. It is recommended that hourly EFs be developed for other countries and power grids.

Response to: Low-carbon cities, GHGs and 'footprints'

We thank Wright and colleagues for their letter in response to our recent paper in Carbon Management [1]. Wright et al. suggest that both their paper defining carbon footprints in general [2] (cited as reference 11 in the article under discussion [1]), as well as their paper defining footprints in the context of cities [3] (cited as reference 22 in the article under discussion), should have been cited in a particular section of our paper (page 472, left column, last paragraph). We agree, we cited both their papers extensively already in our paper, but instead of [2] on page 472, we should have had both [2] and [3], which explicitly addresses city-scale footprints. Regarding the article on cities by Wright et al. [2], an important methodological approach (Ramaswami et al. [4]) was indeed not covered. Ramaswami et al. was the first study to present a different approach to city-scale GHG emissions accounting that looked at supply chains of key infrastructures serving whole cities, considering that cities are both part-producers and part-consumers; that is, city planning requires consideration of city production and consumption activities together [4]. The paper under discussion explicitly details this approach and calls it a transboundary infrastructure supply chain footprint. Such blended approaches (whether case studies or not) are not delineated in Wright et al. [3] and are not the same as the hybrid economic input–output life cycle assessment approach (the third approach cited in [3]). Other very recent papers (e.g., Baynes et al. [5] and Ramaswami et al. [6]) have since recognized the important methodological contribution of these different footprint approaches. These developments reflect the fast-growing and fast-learning nature of our field, which, as researchers, we ourselves are learning better ways of clearly articulating and naming different types of footprints. We thank Wright et al. for recognizing the contribution of these efforts. We clarify that we have attempted to use the words ‘transboundary infrastructure supply-chain footprint’ throughout the article in question [1], and that GHG emissions accounts for CO 2 , CH 4 , N 2 O and others in the Kyoto basket reported in metric tonnes of CO 2 equivalent (mt-CO 2 e). We have tried to stay away from the generic term ‘carbon footprint’. We agree with Wright et al. that the definition of CO 2 e should be clarified and consistent [2]. The use of the term ‘climate footprint’ is also a useful suggestion (incorporating, for example, black carbon); however, the radiative forcing effects of black carbon are not easily incorporated into a mt-CO 2 e framework. Therefore, before using new terms like ‘climate footprint’, we believe more engagement between the scientific and policy communities is needed to yield the best name for the various footprints that are technically sound as well as easily understood by the lay person. We look forward to working with the scientific and policy communities in developing suitable names for the various footprints.