How can data inform your climate change adaptation decisions?

Using data and data science would help where the current change in global life conditions have been directed. However, since the climate change will make many unexpected and unseen changes in our planet it also needs very experienced human intuition. So, basically we should not trust only data.

Our clients continually inspire us by harnessing environmental data to drive transformative change within their organizations and industries. One long-standing partner exemplifies this by leveraging our insights to make informed choices on new sustainability practices. Their achievements are substantial: recycling over 8 million pounds of waste, rerouting 30 million pounds of products to donation centers instead of landfills, transitioning their truck fleet to compressed natural gas, transforming nearly a million pounds of plastic into reusable materials, and slashing carbon emissions by 20% on their journey to net-zero. Such impactful decisions, once merely aspirational, are now grounded in reality, guided by the vital data we provide.

Hello all - we are a specialist climate data and advisory firm. We work a lot with banks in emerging markets. We supply them with climate data that allows them to assess physical climate risks of their clients. This, in theory, should be the start of an engagement on this topic. I believe that banks have an important role to play here. It is also in their own interests to build resilience with their clients, to reduce their own risk over time.

Climate data is not only emission data. Climate data aims to understand how nature works at air, land, water, biodiversity (species) and communities. You need to know how your invesment will impact these. Now we only focused how air will impacted with emissions. IPCC tries to formulate this equation at global scale but we need local and small scale data points. These can be only possible with research actions by universities.

As extreme weather and environmental challenges escalate, using data for prediction and mitigation becomes vital. Gaya Herrington's work with network mathematics, showcased in a recent white paper from Schneider Electric, exemplifies this. By applying graph theory, she assesses interconnected climate risks across behavioral, environmental, and economic spheres. This approach informs our understanding of vulnerabilities and aids in developing targeted adaptation strategies. Herrington's methodology is crucial for strategic decision-making in climate change adaptation, proving essential in our fight against the multifaceted polycrisis. This is just one example of how data can inform our climate change adaptation decisions.

Yes, data is a challenge but we can't wait for perfect data to start the process. The biggest data challenge in our experience is that banks do not know where the assets of their clients are located. But there is a lot of publicly available data out there. But it sometimes requires specialised skills to make sense out of it and turn it into decision useful information.

Data helps our decision process but we are not sure about how to use climate data outputs with limited understanding of nature. The biggest problem is how we will classify materiality. (GreenhouseGases , emissions already formulated by scientists, no more clues)

Data is challenging, but even more so is what that data will be used for. Navigating the complex terrain of environmental data and sustainability is akin to the financial industry's regulatory overhaul post-Great Depression — thrilling yet fraught with challenges. We're in a holding pattern, awaiting the maturation of regulatory frameworks, both here in the US and abroad. Despite the complexities, this shouldn't deter action. On the contrary, it's an opportune moment to be a pioneer, to embed robust environmental data monitoring within your operations. Early adoption not only positions you ahead of the regulatory curve but also integrates sustainable practices into your daily norm, preparing you for when global standards crystallize.

One of the major challenges with using data for climate adaptation is convincing the decision makers to take short term hits for long term benefits. For instance, in CA wildfires have continued to ravage the countryside and they keep getting worse every year. Some time ago there was a referendum in my hometown to raise taxes by a little under $100 per person per year to fund the fire department. In the short term it hurt, but longterm keeping the fire department open would keep insurance premiums down for fire protection. The referendum didn't pass and now years later, instead of paying $100 more everyone's insurance jumped by at-least 2X with average prices upwards of $2500 yearly. Instead of thinking short term we need to think longterm

This involves scrutinizing the sources and methodologies underlying the data, ensuring their reliability and validity. Identifying and addressing data gaps is crucial, prompting exploration of diverse sources and formats. The sheer volume and intricacy of data can be overwhelming, demanding simplification and visualization to enhance clarity and comprehension. The journey with data in climate adaptation is a meticulous one, requiring not just reliance on information but a strategic and discerning engagement with it.

Beginning with the concept that "sustainability" is synonymous with "waste reduction" can pivot a company's perspective toward cost-saving strategies. Enhancing sustainability metrics invariably leads to diminished waste, which translates into significant financial gains. This approach not only appeals to the environmentally conscious but also presents a compelling case for fiscal efficiency that resonates with executives and board members. It's a universal win-win, aligning ecological responsibility with economic benefits—motivating stakeholders across the spectrum by showcasing the tangible rewards of sustainability.

They key thing is that you need data on hazards (what is the probability and severity of a certain hazard happening somewhere - say, a flood or hurricane). But then you also need a sensitivity score. Different asset types and economic activities have different sensitivities to different hazards. Agriculture is sensitive to droughts, but manufacturing less so. If you are a bank, to get around the data challenge of asset locactions, you can also use provinces, regions, or even countries, to calculate the hazards.

The Glacial Lake Outburst Floods (GLOF) risk reduction projects in Northern Pakistan is an example of a data-driven climate adaptation project. Due to rising temperatures, glaciers in Pakistan’s Hindu Kush, Himalayas and Karakorum mountain ranges are melting rapidly. Around 3,044 glacial lakes have developed of which, 33 are prone to hazardous glacial lake outburst flooding (GLOF). These sudden events can release millions of cubic metres of water and debris, leading to the loss of lives, property and livelihoods amongst remote and impoverished mountain communities. Weather-monitoring stations are used to collect meteorological data in catchment areas. This data informs hydrological modelling and helps develop village hazard watch groups.

Climate change is a reality impacting our environments, and data equips us with the insights needed to create sustainable solutions. As a partner at Vardan Environet, I've witnessed the transformative power of data-driven approaches. Initiatives like Climate Ready Boston and Resilient by Design Bay Area Challenge exemplify how data, from climate projections to ecological assessments, can shape a more sustainable future. Let's harness data effectively to reduce emissions and bolster resilience in our communities.

Companies should start with collecting asset location data, and then determine how sensitive each asset is to various climate hazards. Then organize workshops and similate a mock climate disaster. What is the impact? Will your tailing dams hold if you are a mining company? How do you manage the impact of a hurricane? Climate risk should be integrated into risk management, both at banks and non-financial companies.

Localised Impact Analysis- Utilised regional climate data to advise a small island on rising sea levels, leading to the construction of protective sea walls. Agricultural Adjustments- Analysed temperature trends to recommend crop rotation strategies to farmers in temperate zones, enhancing yield despite changing conditions. Infrastructure Planning- Guided a city's infrastructure development using flood prediction models, resulting in resilient urban planning and reduced flood damage. Policy Formulation- Assisted in drafting national environmental policies by interpreting emission data, leading to more stringent carbon reduction targets.

Data plays a critical role in informing climate change adaptation decisions. In a coastal protection project, we used climate and oceanographic data to predict sea-level rise and storm surge patterns. This information guided the design of sea walls and flood defenses. Similarly, in an agricultural project, historical weather data and climate change projections helped in selecting crop varieties resilient to expected temperature and precipitation changes. Data-driven insights also informed our water management strategies, ensuring sustainable use amidst shifting rainfall patterns. Regularly analyzing environmental data allows for dynamic adaptation strategies, ensuring they remain effective as climate conditions evolve.

Not just about adaptation but climate change in general, I've found that people connect most with stories. Data is useful for planning and decision making but what moves ordinary people most is story telling. Seeing people like them going through climate effects or explaining complex concepts in simple easy to understand language. Engaging with people on a level basis, that'll likely get more action than figures and numbers. Data has its place in of course, but even adaptation needs social acceptability - people.