The carbon footprint of household energy use in the United States.

Residential energy use accounts for roughly 20% of greenhouse gas (GHG) emissions in the United States. Using data on 93 million individual households, we estimate these GHGs across the contiguous United States and clarify the respective influence of climate, affluence, energy infrastructure, urban form, and building attributes (age, housing type, heating fuel) in driving these emissions. A ranking by state reveals that GHGs (per unit floor space) are lowest in Western US states and highest in Central states. Wealthier Americans have per capita footprints ∼25% higher than those of lower-income residents, primarily due to larger homes. In especially affluent suburbs, these emissions can be 15 times higher than nearby neighborhoods. If the electrical grid is decarbonized, then the residential housing sector can meet the 28% emission reduction target for 2025 under the Paris Agreement. However, grid decarbonization will be insufficient to meet the 80% emissions reduction target for 2050 due to a growing housing stock and continued use of fossil fuels (natural gas, propane, and fuel oil) in homes. Meeting this target will also require deep energy retrofits and transitioning to distributed low-carbon energy sources, as well as reducing per capita floor space and zoning denser settlement patterns.

Deforestation, certification, and transnational palm oil supply chains: Linking Guatemala to global consumer markets.

Although causal links between tropical deforestation and palm oil are well established, linking this land use change to where the palm oil is actually consumed remains a distinct challenge and research gap. Supply chains are notoriously difficult to track back to their origin (i.e., the 'first-mile'). This poses a conundrum for corporations and governments alike as they commit to deforestation-free sourcing and turn to instruments like certification to increase supply chain transparency and sustainability. The Roundtable on Sustainable Palm Oil (RSPO) offers the most influential certification system in the sector, but whether it actually reduces deforestation is still unclear. This study used remote sensing and spatial analysis to assess the deforestation (2009-2019) caused by oil palm plantation expansion in Guatemala, a major palm oil source for international consumer markets. Our results reveal that plantations are responsible for 28% of deforestation in the region and that more than 60% of these plantations encroach on Key Biodiversity Areas. RSPO-certified plantations, comprising 63% of the total cultivated area assessed, did not produce a statistically significant reduction in deforestation. Using trade statistics, the study linked this deforestation to the palm oil supply chains of three transnational conglomerates - Pepsico, Mondelez International, and Grupo Bimbo - all of whom rely on RSPO-certified supplies. Addressing this deforestation and supply chain sustainability challenge hinges on three measures: 1) reform of RSPO policies and practices; 2) robust corporate tracking of supply chains; and 3) strengthening forest governance in Guatemala. This study offers a replicable methodology for a wide-range of investigations that seek to understand the transnational linkages between environmental change (e.g. deforestation) and consumption.

Food production and resource use of urban farms and gardens: a five-country study.

There is a lack of data on resources used and food produced at urban farms. This hampers attempts to quantify the environmental impacts of urban agriculture or craft policies for sustainable food production in cities. To address this gap, we used a citizen science approach to collect data from 72 urban agriculture sites, representing three types of spaces (urban farms, collective gardens, individual gardens), in five countries (France, Germany, Poland, United Kingdom, and United States). We answered three key questions about urban agriculture with this unprecedented dataset: (1) What are its land, water, nutrient, and energy demands? (2) How productive is it relative to conventional agriculture and across types of farms? and (3) What are its contributions to local biodiversity? We found that participant farms used dozens of inputs, most of which were organic (e.g., manure for fertilizers). Farms required on average 71.6 L of irrigation water, 5.5 L of compost, and 0.53 m2 of land per kilogram of harvested food. Irrigation was lower in individual gardens and higher in sites using drip irrigation. While extremely variable, yields at well-managed urban farms can exceed those of conventional counterparts. Although farm type did not predict yield, our cluster analysis demonstrated that individually managed leisure gardens had lower yields than other farms and gardens. Farms in our sample contributed significantly to local biodiversity, with an average of 20 different crops per farm not including ornamental plants. Aside from clarifying important trends in resource use at urban farms using a robust and open dataset, this study also raises numerous questions about how crop selection and growing practices influence the environmental impacts of growing food in cities. We conclude with a research agenda to tackle these and other pressing questions on resource use at urban farms. Supplementary Information: The online version contains supplementary material available at 10.1007/s13593-022-00859-4.

Where does your guacamole come from? Detecting deforestation associated with the export of avocados from Mexico to the United States.

The United States (U.S.) imports 87 percent of its avocados from a single Mexican region, Michoacán. Although environmental and social costs associated with avocado production are significant, consumers and retailers in the U.S. cannot clearly discern them in part due to complex, opaque supply chains. In this paper, we use a novel methodology, TRAcking Corporations Across Space and Time (TRACAST), to reconstruct avocado supply chains between the U.S. retailers and Mexican producers and exporters. Using remote sensing and machine learning, we document how avocado plantations are associated with deforestation in Michoacán, whose forests are important reservoirs for biodiversity, especially for the Monarch butterfly (Danaus plexippus). We estimate that ~20% of the total deforestation in Michoacán between 2001 and 2017 is associated with the expansion of avocado plantations. Despite these impacts, interviews reveal that industry associates (namely, representatives of firms and associations) do not consider avocado production to be a driver of deforestation in the region. This disconnect between actual and perceived environmental impacts can be addressed by the U.S. governmental agencies that play influential roles in regulating avocado imports for sanitary and health purposes and by the vertically integrated avocado trading firms that connect Michoacán packing houses to Kroger, Costco, and other prominent U.S. grocers. Key measures to make the U.S.-Mexico avocado supply chain more sustainable include conventional regulatory tools, greater transparency, and improved governance through multi-stakeholder initiatives.

Quantifying the Urban Food-Energy-Water Nexus: The Case of the Detroit Metropolitan Area.

The efficient provision of food, energy, and water (FEW) resources to cities is challenging around the world. Because of the complex interdependence of urban FEW systems, changing components of one system may lead to ripple effects on other systems. However, the inputs, intersectoral flows, stocks, and outputs of these FEW resources from the perspective of an integrated urban FEW system have not been synthetically characterized. Therefore, a standardized and specific accounting method to describe this system is needed to sustainably manage these FEW resources. Using the Detroit Metropolitan Area (DMA) as a case, this study developed such an accounting method by using material and energy flow analysis to quantify this urban FEW nexus. Our results help identify key processes for improving FEW resource efficiencies of the DMA. These include (1) optimizing the dietary habits of households to improve phosphorus use efficiency, (2) improving effluent-disposal standards for nitrogen removal to reduce nitrogen emission levels, (3) promoting adequate fertilization, and (4) enhancing the maintenance of wastewater collection pipelines. With respect to water use, better efficiency of thermoelectric power plants can help reduce water withdrawals. The method used in this study lays the ground for future urban FEW analyses and modeling.

The social anatomy of climate change denial in the United States.

Using data from Twitter (now X), this study deploys artificial intelligence (AI) and network analysis to map and profile climate change denialism across the United States. We estimate that 14.8% of Americans do not believe in climate change. This denialism is highest in the central and southern U.S. However, it also persists in clusters within states (e.g., California) where belief in climate change is high. Political affiliation has the strongest correlation, followed by level of education, COVID-19 vaccination rates, carbon intensity of the regional economy, and income. The analysis reveals how a coordinated social media network uses periodic events, such as cold weather and climate conferences, to sow disbelief about climate change and science, in general. Donald Trump was the strongest influencer in this network, followed by conservative media outlets and right-wing activists. As a form of knowledge vulnerability, climate denialism renders communities unprepared to take steps to increase resilience. As with other forms of misinformation, social media companies (e.g., X, Facebook, YouTube, TikTok) should flag accounts that spread falsehoods about climate change and collaborate on targeted educational campaigns.

A Mixed Application of Geographically Weighted Regression and Unsupervised Classification for Analyzing Latex Yield Variability in Yunnan, China.

This paper introduces a mixed method approach for analyzing the determinants of natural latex yields and the associated spatial variations and identifying the most suitable regions for producing latex. Geographically Weighted Regressions (GWR) and Iterative Self-Organizing Data Analysis Technique (ISODATA) are jointly applied to the georeferenced data points collected from the rubber plantations in Xishuangbanna (in Yunnan province, south China) and other remotely-sensed spatial data. According to the GWR models, Age of rubber tree, Percent of clay in soil, Elevation, Solar radiation, Population, Distance from road, Distance from stream, Precipitation, and Mean temperature turn out statistically significant, indicating that these are the major determinants shaping latex yields at the prefecture level. However, the signs and magnitudes of the parameter estimates at the aggregate level are different from those at the lower spatial level, and the differences are due to diverse reasons. The ISODATA classifies the landscape into three categories: high, medium, and low potential yields. The map reveals that Mengla County has the majority of land with high potential yield, while Jinghong City and Menghai County show lower potential yield. In short, the mixed method can offer a means of providing greater insights in the prediction of agricultural production.

The 'Geographic Emission Benchmark' model: a baseline approach to measuring emissions associated with deforestation and degradation.

This paper proposes a new land-change model, the Geographic Emission Benchmark (GEB), as an approach to quantify land-cover changes associated with deforestation and forest degradation. The GEB is designed to determine 'baseline' activity data for reference levels. Unlike other models that forecast business-as-usual future deforestation, the GEB internally (1) characterizes 'forest' and 'deforestation' with minimal processing and ground-truthing and (2) identifies 'deforestation hotspots' using open-source spatial methods to estimate regional rates of deforestation. The GEB also characterizes forest degradation and identifies leakage belts. This paper compares the accuracy of GEB with GEOMOD, a popular land-change model used in the UN-REDD (Reducing Emissions from Deforestation and Forest Degradation) Program. Using a case study of the Chinese tropics for comparison, GEB's projection is more accurate than GEOMOD's, as measured by Figure of Merit. Thus, the GEB produces baseline activity data that are moderately accurate for the setting of reference levels.