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.

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.

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 Random Forest-Cellular Automata modelling approach to explore future land use/cover change in Attica (Greece), under different socio-economic realities and scales.

This paper explores potential future land use/cover (LUC) dynamics in the Attica region, Greece, under three distinct economic performance scenarios. During the last decades, Attica underwent a significant and predominantly unregulated process of urban growth, due to a substantial increase in housing demand coupled with limited land use planning controls. However, the recent financial crisis affected urban growth trends considerably. This paper uses the observed LUC trends between 1991 and 2016 to sketch three divergent future scenarios of economic development. The observed LUC trends are then analysed using 27 dynamic, biophysical, socio-economic, terrain and proximity-based factors, to generate transition potential maps, implementing a Random Forests (RF) regression modelling approach. Scenarios are projected to 2040 by implementing a spatially explicit Cellular Automata (CA) model. The resulting maps are subjected to a multiple resolution sensitivity analysis to assess the effect of spatial resolution of the input data to the model outputs. Findings show that, under the current setting of an underdeveloped land use planning apparatus, a long-term scenario of high economic growth will increase built-up surfaces in the region by almost 24%, accompanied by a notable decrease in natural areas and cropland. Interestingly, in the case that the currently negative economic growth rates persist, artificial surfaces in the region are still expected to increase by approximately 7.5% by 2040.