Data for climate decision- and policymaking

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    Alf

Environmental technology concept. Sustainable development goals. SDGs.

By employing digital technologies, mitigating the effects of and fighting climate change has become easier. However, in order to optimize climate decision and policymaking, governments and policymakers are beginning to make use of data, especially big data. Data-driven policymaking is the process of using ICTs to incorporate data and data analysis into the decision-making process. By doing so, policies can be tailormade to cater to the needs of citizens, based on scientific analyses, resulting in better and more efficient decisions, policies and results. Data-driven policymaking takes evidence-based policymaking one step further. Moreover, data-driven policymaking collaborates and co-creates policy with citizens, thereby enhancing transparency and trust in government.

Data-driven decision-making is currently making its way in development cooperation. In fact, through the adoption of the ‘twin transition’ by the BMZ, reducing greenhouse gas emissions through a data-based sustainable energy and mobility planning is a top goal. To achieve this, data gaps will be addressed, real-time information and predictive analytics will be generated or data sets and analyses will be made more accessible, re-usable, and trustworthy. Open climate data repositories like the EU-funded Climate Data Store of the Copernicus Climate Change Service collect and provide data. The project provides quality-assured information about the past, current and future states of the climate in Europe and worldwide to support European adaptation and mitigation policies in a number of sectors. In addition, many governments have been creating so-called Policy Labs in order to enable experimentations between stakeholders and citizens. Similarly, the GIZ Data Lab is a platform collaborating with different partners in order to utilize data to achieve sustainable development in partner countries. The GIZ Data Lab experiments with different types of data (e.g. satellite, mobile data) and develops different data-based models to achieve sustainable development. It does so while making sure not to breach data privacy.

Reducing pollution by traffic management

Development cooperation is using data in forming policy with the aim of reducing factors contributing to climate change. One example is the MOVES-Mexico (Motor Vehicles Emissions Simulator) model. The USAID supported model has been implemented in Mexico City in order to manage traffic and thereby reduce pollution. The research team from the University of California and from the Instituto Nacional de Ecología y Cambio Climático in Mexico analyzed different transport electrification possibilities based on data collected by the GPS navigation software Waze. The data provided information about where and for how long traffic jams occur all over the city. The team could, therefore, extract information about the released emissions. Moreover, the team relied on ‘popular times’ data from Google to track urban population movement. Thus, the team was able to determine the most suitable policy options and locations for electric vehicle charging stations.

Water saving

Data-driven decision-making does not always have to be on a large governmental scale. It is sometimes also used on an individual scale. For example, the GIZ Green Innovation Centres project in Tunisia allows for individual farmers to save water on irrigation as water can be quite scarce all over the country, but especially in the highlands. Through this project, farmers have access to data on soil, climate and plants through a software application. This app not only functions as a storage location for collected data, it also calculates exactly how much water certain plants need and what time is best for watering these plants. Hence, this app can make suggestions for farmers and help them make decisions on how to optimize their farming methods and how they can use water in a targeted matter, thereby irrigating more sustainably.

AI training data to tackle the climate crisis

The FAIR Forward project cooperates with the Lacuna Fund to develop datasets and AI models with and for local communities most affected by climate change.

The Data Powered Positive Deviance (DPPD) approach, which has been developed by the DPPD Network, focuses on benefitting from non-traditional data to identify positive deviants and in a second step explore their solutions in order to potentially scale them. The DPPD Network was initiated in 2020 by the GIZ Data Lab, the UN Global Pulse Lab Jakarta, the UNDP Accelerator Labs Network and the University of Manchester’s Centre for Digital Development.

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