Our Mission
We buy land in places that will experience a net benefit from the changes in global climate.
We aspire to be landowners at a scale that provides a level of pricing control in selected markets.
We provide initial returns by re-wilding and selling carbon offsets, offering locations for wind and solar providers, managing timber and any extraction resources, and then developing small scale mixed-use communities connected in meaningful ways to the existing economy.
We rent and sell real estate in these communities for buyers looking for a long term resilient investment that will benefit multiple generations.
The Science
The goal of our proprietary climate analog mapping at Resilient Land Company is to quantify and communicate the nature and magnitude of expected climate change in a specific geographic location - as well as associated risks and challenges that could be caused by these changes. Climate analog analyses can answer questions such as: What geographies have an existing climate that is most similar to the expected future climate of another location of interest, or alternatively, what geographies have an expected future climate that is most similar to an existing climate in a location of interest?
The analog method is unique in that it provides a practical frame of reference for decision makers regardless of their level of sophistication with climate science and climate models. The explanation, “New York’s climate in 2080 will look and feel like the existing climate of Alabama.” is an intuitive and relatable way to explain the expected magnitude and nature of climate change and what adaptations may be required. Combined with predictive analysis of other related hazards such as seismic activity, hurricane/typhoon risk, and sea level rise ranges, analog mapping becomes a highly useful site selection tool for companies making very long term investments in real property and development. This sophisticated analysis is what drives the real estate decisions for the RLC mixed-use communities. Our developments and placemaking only occur in the most climatically resilient and safest geographies.
Climate analog mapping is based on the measurement of similarity between a location’s future climate and the current climate of another region using a metric called “sigma dissimilarity”. Because sigma is a measure of dissimilarity, the lower the value the better the match between the climates of two locations and/or times. For example, if a region has a low value of sigma dissimilarity, then that region’s current climate is very similar to the future climate of the location for which the comparison is being made. More precisely - and using London as an example - a sigma dissimilarity of zero between a region's current climate and that of London in 2080 would indicate a perfect match. As sigma dissimilarity increases, the utility of a region to serve as a climate analog for another region decreases, with values of sigma dissimilarity greater than ~2 indicating that there is little in common between a location’s future climate and current climates elsewhere. For climate analog mapping purposes, sigma dissimilarity is typically calculated using numerous climate variables representing seasonal and annual means and variability of temperature and precipitation (e.g., average maximum and minimum temperature in summer and winter and average precipitation in summer and winter).
Because how climate will change in the future is uncertain and not linear, robust analyses require that climate analog mapping consider multiple forecasts of future climate. Different forecasts of future climate arise from two main sources: (1) differences between the various earth systems models (ESMs) that are used to generate simulations of future climate trajectories and (2) different scenarios regarding how much greenhouse gas concentrations are expected to change in the future. The emissions scenarios are termed “Shared Socioeconomic Pathways” (SSPs), which are narratives describing alternative scenarios for future socio-economic developments. Our analyses considered forecasts from five different ESMs and two emissions scenarios: SSP3.7-0 (SSP370) and SSP5.8-5 (SSP585). SSP370 results in a doubling of CO2 emissions by 2100 and a “very likely” increase of global mean temperature in the range of 2.8-4.6 °C (5-8.3 °F) by 2080. SSP585 might be considered a “worst case” scenario, but unfortunately aspects of which align with current trajectories. Under SSP585 CO2 emissions are assumed to triple by 2075, resulting in a very likely increase of global mean temperature in the range of 3.3-5.7 °C (5.9- 10.3°F) by 2080. In addition to the two SSPs and the five forecasts from the ESMs, we also considered the average of these five forecasts for each scenario, resulting in a total of 12 analyses.
For each of the 12 future climate scenarios, sigma dissimilarity between each geography’s current climate and future climate for every location on earth was calculated. The resulting sigma values were mapped and the region containing the largest areas with a sigma dissimilarity less than two were identified. These regions are expected to harbor climates in the future most similar to the present-day climate of the geography for which the comparison was made. In other words, these are the locations with the most “resilient” climates – the ones that are least impacted by the climate changes between now and 2080.Are your customers raving about you on social media? Share their great stories to help turn potential customers into loyal ones.
The Team
