Doha: As Qatar pursues a sustainable future, efforts in this direction enter an opportunistic new phase following a renewed emphasis on leadership and accountability. The recently announced National Climate Change Action Plan (NCCAP) is primed to propel the country closer to achieving a 25 percent reduction in greenhouse gas (GHG) emissions by 2030, which also aligns with its overall 2030 National Vision and national development strategy, said Tomas Estevez, Partner at Boston Consulting Group, Doha and Digital/AI Head of BCG Qatar.
“In itself, this is a clear indication of decarbonization intent at the national level. But while several foundations are now in place for this eventuality, other integral enablers require integration before the climate code can truly be conquered, including Artificial Intelligence (AI),” Estevez told The Peninsula.
He added that despite continuous decarbonization advancements to date, the fact that solely diversifying to low-carbon energy alternatives will not be enough to adequately limit GHG emissions has already been acknowledged. This primarily stems from the oil and gas (O&G) industry, which entails the entire emissions value chain.
“Investments into technology in efforts to combat climate challenges is an ongoing effort and one that is seen to be prioritised in Qatar, with recent announcements of spending $200m on emissions reduction technologies,” he added. “For Qatari O&G companies to continue driving their decarbonization and operational optimization and visibility efforts, these far-reaching tools, together with AI and machine learning (ML), are imperative. With their support and capabilities, companies can meet severalobjectives simultaneously and continuously – identifying emissions sources, restricting energy consumption, bolstering organizational energy efficiency,” he said.
Globally, these technologies have already dispelled any hypothetical notions, demonstrating their value in supporting GHG reduction activities. While these use cases offer additional inspiration, companies aspiring to emulate their success do require full data integration, a process that begins by developing an accurate, acceptable, and high-calibre GHG emissions baseline. Created through the collection and analysis of real-time operational data and transaction-based data from suppliers and customers, such baselines empower O&G players to meet the objectives mentioned above, as well as uncover abatement initiatives.
That being said, other challenges inevitably await, including calculating, limiting, and observing methane emissions – a particularly complex problem where considerable progression can be driven through AI.
AI is at the forefront of solving the emissions challenge in Qatar, said Estevez. “Even before the latest GHG reduction commitment, AI was critical to abatement journeys – accurately estimating emissions levels, driving reductions by optimizing operations, and monitoring progress by bringing together diverse data sources, and incorporating advanced algorithms,” said Estevez.
“Building a robust and reliable baseline for this scope and working with suppliers and customers to abate related emissions is an intricate analytical hurdle encouraging companies to onboard AI-based solutions,” he added.
When attempting to curve GHG outputs, companies’ baselining efforts often focus predominantly on emissions projections across scopes 1 and 2. Moving forward, the process must cover all operations and scopes across the value chain, taking into account suppliers, customers, production forecasts, production conclusion information, and growth opportunities. Local companies should ensure new and future abatement projects align with Qatar’s NCCAP objectives.
When focusing on new business opportunities, many companies often experience difficulty managing decarbonization efforts. Integrating digital abatement tools with data architecture can break this trend, enabling change management programs that enhance operating models, embrace new ways of working, and facilitate desired emissions results.
“As Qatari O&G companies attempt to abate GHG emissions across their overall operational scope, such strategies’ success depends on various success factors, one of most important pertaining to AI and ML-based tools and techniques. Therefore, ensuring integration at the earliest opportunity is essential,” he said.
