• Atmospheric CO2 /Parts per Million /Annual Averages /Data Source: noaa.gov

  • 1980338.91ppm

  • 1981340.11ppm

  • 1982340.86ppm

  • 1983342.53ppm

  • 1984344.07ppm

  • 1985345.54ppm

  • 1986346.97ppm

  • 1987348.68ppm

  • 1988351.16ppm

  • 1989352.78ppm

  • 1990354.05ppm

  • 1991355.39ppm

  • 1992356.1ppm

  • 1993356.83ppm

  • 1994358.33ppm

  • 1995360.18ppm

  • 1996361.93ppm

  • 1997363.04ppm

  • 1998365.7ppm

  • 1999367.8ppm

  • 2000368.97ppm

  • 2001370.57ppm

  • 2002372.59ppm

  • 2003375.14ppm

  • 2004376.96ppm

  • 2005378.97ppm

  • 2006381.13ppm

  • 2007382.9ppm

  • 2008385.01ppm

  • 2009386.5ppm

  • 2010388.76ppm

  • 2011390.63ppm

  • 2012392.65ppm

  • 2013395.39ppm

  • 2014397.34ppm

  • 2015399.65ppm

  • 2016403.09ppm

  • 2017405.22ppm

  • 2018407.62ppm

  • 2019410.07ppm

  • 2020412.44ppm

  • 2021414.72ppm

  • 2022418.56ppm

  • 2023421.08ppm

Partner Content

Challenges to net-zero infrastructure investing

Darwin Marcelo from EDHECinfra discusses how to overcome issues involving data and metrics to reach net-zero emissions in infrastructure investments.

Infrastructure investments are receiving a lot of attention and for good reason. In the US, the Inflation Reduction Act stands to provide a massive boost to renewable energy infrastructure creation. In the UK and Europe, there is much talk about ensuring that alternative energy sources, such as wind turbines, will deliver the watts necessary to power our future.

But there’s not enough attention paid to the obstacles that stand in the way of reaching net-zero emissions via infrastructure investments and how to overcome them.

Baseline metrics

First, if you want to track progress toward net zero in infrastructure, you need a robust and systematic set of baseline carbon-emissions metrics. Without a baseline, it will not be possible to determine the effectiveness of investments or measures taken to reduce emissions.

Baseline metrics are also vital for comparing the financial performance and carbon footprint of an asset to identify opportunities and threats, such as high-emitting assets that will need retrofitting or renovation to reach net zero.

Accurate data

Accurate and comprehensive data on emissions, energy consumption, and other climate-related factors is crucial to achieving net-zero carbon emissions. However, today, such data is not readily available for most infrastructure subsectors, especially unlisted assets and companies.

To solve this problem, EDHECinfra, came up with estimations for Scopes 1, 2, and 3 emissions across more than 60 TICCS categories, covering more than 700 infrastructure assets or companies in 23 countries. TICCS stands for The Infrastructure Company Classification Standard (TICCS), a new set of categories that replaces those inherited from the private equity and real estate industries.


[A] lack of consistency in measurement makes it difficult to compare the performance of different infrastructure assets and to identify areas that show promise.

Darwin Marcelo, project director, EDHECinfra

Standardised metrics

Another hurdle we must overcome is the lack of standardised methods and metrics for measuring emissions in the infrastructure sector. This lack of consistency in measurement makes it difficult to compare the performance of different infrastructure assets and to identify areas that show promise.

Research by EDHECinfra shows that in the infrastructure sector alone, there are more than 17 reporting standards and frameworks to report and/or comply with ESG-related data requirements. Developing and implementing standardised emissions measurement metrics is essential to track progress toward net zero and facilitate effective decision-making.

This is why EDHECinfra created the ESG Meta Standard, a database that maps and categorises 700 metrics to help investors understand how to classify ESG risk and impact.


Most of the climate data that governments, banks and other institutions use to track progress is inaccurate.

Darwin Marcelo, project director, EDHECinfra

Data quality

Data quality is also paramount in tracking progress and enabling effective climate-related decision-making. But, unfortunately, most of the climate data that governments, banks and other institutions use to track progress is inaccurate.

Why is this? Today, we expect companies to self-report emissions data, and, too often, these reports lack precise data. Any action we take risks failure if we don’t have reliable data. An alternative to self-reporting and shoddy data is estimation by machine learning algorithms and the use of massive, open-source data caches.

At EDHECinfra, we used AI and carbon emission estimates to create our infraMetrics infrastructure investment analytics platform, which is set to go live in April.

Asset lifespan

Finally, there’s asset lifespan. Infrastructure assets have long lifecycles – 20, 50, 100 years – so the decisions we make today will have an impact on generations to come. There is most definitely a need for long-term data generation and analysis systems to help guide infrastructure investment decisions and ensure that the goal of net-zero emissions is achieved.

These issues present an impressive barrier to net zero via infrastructure investment, one that researchers are tackling, one by one, at EDHECinfra. By bringing together big data, machine learning, and sector expertise, we are confident that we will make an impact.

Darwin Marcelo is project director, Green Infrastructure Team, EDHECinfra.

Content Tags: Infrastructure  Energy  Emissions 
Sponsored by EDHECinfra

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