We, our partners, and users of our services deploy cookies and similar technologies to record certain information, as well as the options you as Data Subject (“means any identified or identifiable individual to whom personal data relates”) have to control them. What are cookies? Cookies are small pieces of data, stored in text files, that are stored on your computer or another device when websites are loaded in a browser to make the user’s experience more efficient. Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. 

Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe.

Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe.

Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe.

Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe.

Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe.

Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe.  Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe.Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. 

IISD is a technical partner in the ASEAN Low Carbon Energy Programme (LCEP), which aims to drive inclusive growth and poverty reduction through increased energy efficiency and the adoption of low-carbon energy. The ASEAN LCEP is a GBP 15 million overseas development program under the auspices of the United Kingdom’s Prosperity Fund, which is targeted specifically at six countries: Indonesia, Malaysia, the Philippines, Thailand, and Vietnam. The program seeks to help these countries reap the sustainable development opportunities that can arise from the deployment of low-carbon energy, in particular through green finance and energy efficiency. The main project partners are Ernst & Young, IMC worldwide, and the Carbon Trust. The ASEAN LCEP program runs until 2022.

IISD is a technical partner in the ASEAN Low Carbon Energy Programme (LCEP), which aims to drive inclusive growth and poverty reduction through increased energy efficiency and the adoption of low-carbon energy. The ASEAN LCEP is a GBP 15 million overseas development program under the auspices of the United Kingdom’s Prosperity Fund, which is targeted specifically at six countries: Indonesia, Malaysia, the Philippines, Thailand, and Vietnam. The program seeks to help these countries reap the sustainable development opportunities that can arise from the deployment of low-carbon energy, in particular through green finance and energy efficiency. The main project partners are Ernst & Young, IMC worldwide, and the Carbon Trust. The ASEAN LCEP program runs until 2022.

Renewable energy, often referred to as clean energy, comes from natural sources or processes that are constantly replenished. For example, sunlight and wind keep shining and blowing, even if their availability depends on time and weather.

While renewable energy is often thought of as a new technology, harnessing nature’s power has long been used for heating, transportation, lighting, and more. Wind has powered boats to sail the seas and windmills to grind grain. The sun has provided warmth during the day and helped kindle fires to last into the evening. But over the past 500 years or so, humans increasingly turned to cheaper, dirtier energy sources, such as coal and fracked gas.

Now that we have innovative and less-expensive ways to capture and retain wind and solar energy, renewables are becoming a more important power source, accounting for more than 12 percent of U.S. energy generation. The expansion in renewables is also happening at scales large and small, from giant offshore wind farms to rooftop solar