In the Paris Agreement, Turkey pledged to reduce greenhouse gas (GHG) emissions by 21% by 2030 relative to business-as-usual (BAU). However, Turkey relies heavily on imported energy and fossil-intensive power generation. And despite significant wind and solar energy potential, only 5.1% of its total power is generated by wind and solar installations. Finally, although two nuclear power stations are planned, no nuclear capacity currently exists.

This study is based on an expectation that to fulfill its Paris Agreement pledge, Turkey will likely need to reduce its reliance on fossil-based energy and make additional investments in low-carbon energy sources—moves that may impact the nation’s GDP, electricity generation profiles and resulting carbon prices. To fully assess these impacts, the researchers develop a computable general equilibrium (CGE) model of the Turkish economy that combines macroeconomic representation of non-electric sectors with a detailed representation of the electricity sector. They analyze several scenarios to assess the impact of an emission trading scheme in Turkey: one including the planned nuclear development and renewable subsidy scheme (BAU), the other in which no nuclear technology is allowed (NoN).

The assessment shows that in 2030, without an emissions trading policy, the primary energy mix will consist mainly of oil, natural gas and coal. Under an emission trading scheme, however, coal-fired power generation vanishes by 2030 in both BAU and NoN due to the high cost of carbon. With nuclear (BAU), GHG emissions are 3.1% lower than NoN due to the resulting energy mix, allowing for a lower carbon price ($50/tCO₂ in BAU compared to $70/tCO₂ in NoN). These results suggest that fulfillment of Turkey’s Paris Agreement pledge may be possible at a modest economic cost of about 0.8–1% of GDP by 2030.

The ability to separate out a distinct signal from ambient noise in reams of scientific data is critical to detecting a meaningful trend or turning point in the data. That’s especially true when it comes to identifying signals of improving or declining air quality trends, whose magnitude can be smaller than that of underlying natural variations or cycles in chemical, meteorological and climatological conditions. This discrepancy makes it challenging to track how concentrations of surface air pollutants such as ozone change as a result of policies or other causes within a particular geographical region or timeframe.

A case in point is any attempt to estimate whether there has been any change in summertime mean ozone concentration over the Northeastern U.S., which can vary from place to place as well as from year to year. To obtain a reliable estimate in the most computationally efficient manner, one would need to know the minimum geographical area required to capture the full range of localized ozone concentrations, as well as the minimum number of years to sample to rule out short-term natural variability of atmospheric conditions—such as an abnormally hot summer or El Niño year—that may skew the numbers.

Now a team of researchers from the MIT Joint Program on the Science and Policy of Global Change and collaborating institutions has developed a method to optimize air quality signal detection capability over much of the continental U.S. by applying a strategic combination of spatial and temporal averaging scales. Presented in a study in the journal Atmospheric Chemistry and Physics, the method could improve researchers’ and policymakers’ understanding of air quality trends and their ability to evaluate the efficacy of existing and proposed emissions-reduction policies.