Recent years have seen an increase in the use of secondary data in climate adaptation research. While these valuable datasets have proven to be powerful tools for studying the relationships between people and their environment, they also introduce unique oversights and forms of invisibility, which have the potential to become endemic in the climate adaptation literature. This is especially dangerous as it has the potential to introduce a double exposure where the individuals and groups most likely to be invisible to climate adaptation research using secondary datasets are also the most vulnerable to climate change. Building on significant literature on invisibility in survey data focused on hard-to-reach and under-sampled populations, we expand the idea of invisibility to all stages of the research process. We argue that invisibility goes beyond a need for more data. The production of invisibility is an active process in which vulnerable individuals and their experiences are made invisible during distinct phases of the research process and constitutes an injustice. We draw on examples from the specific subfield of environmental change and migration to show how projects using secondary data can produce novel forms of invisibility at each step of the project conception, design, and execution. In doing so, we hope to provide a framework for writing people, groups, and communities back into projects that use secondary data and help researchers and policymakers incorporate individuals into more equitable climate planning scenarios that “leave no one behind.”

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The need to protect communities from hazardous waste is an important agenda for any nation. Although pollutant management and policy development are attempted in many developing countries, it is not always successful due to limited funds, project resources, and access to trained experts to conduct toxic site identification projects. For this reason, Pure Earth created the Toxic Site Identification Program (TSIP). The goal of the TSIP program is to provide reliable information and data that identifies location of toxic sites and the level of toxic severity. TSIP is significant because it provides developing countries a database of ranked toxic sites identified as hazardous risk to human health. For example, Azerbaijan is one of the most polluted post-Soviet nations, but has limited resources to address and manage its polluted sites. The Azerbaijani TSIP database is the first reliable data source that identifies hazardous pollutants in the country. Our study is significant because it discusses how the TSIP labels and ranks the level of toxic severity to human health. It is also the first data source in Azerbaijan that identifies which Soviet legacy toxic sites are affecting local communities. Although our study is specific to Azerbaijan, the TSIP method can be applied to nations with similar data limitations and the need for a database that identifies country specific environmental and hazardous locations. The data sampling method and results are mapped and accompanied by tables of the collected pollutant types to identify communities at greatest health-risk to legacy toxic sites.

Relating social inequality and vulnerability to environmental hazards is an especially challenging task in regions with a paucity of data. Researchers attempting to measure the potential environmental and human impacts of past and continuing industrial toxicity in Azerbaijan have often either questioned the reliability of environmental indicators disclosed by the state’s official statistics or found the government’s environmental and population data partial and incomplete. To contribute to a clearer description of the human impacts of toxic waste locations and to assist other researchers, we use a novel methodology. By overlaying data from Azerbaijan’s Toxic Site Identification Program (TSIP) onto national census population data – augmented with in-country interviews – we can map the inequitable distribution of infant mortality, unemployment, and toxic waste sites to better suggest some of the places and people in particular need of environmental mitigation and health, and economic intervention. This method is transferable to future research in the Caucasus, Eurasia, and other data- poor areas.

What is an effective approach to address wastewater treatment within low-and middle-income countries (LMICs)? To answer this question, we developed an integrated lake management (ILM) model which proves to reduce the pollution levels in our study site, Lake Khojasan basin, located in LMIC Azerbaijan. We found that the inflow of the treated wastewater into the lake can be a reliable approach to effectively restore the lake's ecosystem. Our model suggests that treated wastewater may gradually replace polluted water from the lake and support its full rehabilitation while at the same time restoring neighboring water systems. Our ILM is based on our calculated water and pollutant balance equations. According to our model, the increased investment around the lake will lead to an improvement of the treated water. From the results of this work, future studies may expand upon our cost-effective integrated lake management (ILM) model when using natural inflow patterns into wetlands to purify the water basin. Our study provides a model for researchers to use or expand upon when implementing sustainable and eco-friendly methods that can control highly polluted and mismanaged lakes within LMICs.