Correction to: Renewable energy utilization to promote sustainability in GCC countries: policies, drivers, and barriers

In the abstract, the statement "The GHG emissions avoidance expected to be achieved by the GCC countries will vary between 5 and 247 million tons of CO equivalent by 2030."

Renewable energy utilization to promote sustainability in GCC countries: policies, drivers, and barriers

There is a growing focus on the role of renewable energy (RE) policies such as feed-in tariffs (FITs), renewable portfolio standards (RPSs), subsidies, incentives, and research and development in the global energy policy mix and in promoting environmental sustainability. Although most developed countries have well-formulated RE policies, in developing countries, such policies face many barriers. This study analyzes the policies, drivers, and barriers to RE deployment for fostering environmental sustainability in the Gulf Cooperation Council (GCC) countries. In the GCC region, the need for economic diversification to reduce dependency on single resource, diminishing hydrocarbon reserve, loss of oil export revenue, climate change mitigation pledges, and abundant solar energy resource are the key drivers for diversifying energy sources to include RE. However, the apparent lack of consolidated policy framework for wide-scale RE utilization calls for a well-articulated policy to advance RE development in each member state. Although FIT and RPS approaches could be effective for initial deployment of small-scale RE projects, a competitive tendering and auctioning mechanisms are more suitable for large-scale projects. Whereas, developing effective energy codes could successfully promote RE deployment, the increased share of RE in energy supply would have synergistic impacts on the region. The GHG emissions avoidance expected to be achieved by the GCC countries will vary between 5 and 247 million tons of CO₂ equivalent by 2030. The fulfillment of inspirational RE targets for 2030 would contribute in fulfilling climate change mitigation pledges, environmental sustainability, economic growth, and generating new jobs.

Ozone levels in the Empty Quarter of Saudi Arabia--application of adaptive neuro-fuzzy model

In arid regions, primary pollutants may contribute to the increase of ozone levels and cause negative effects on biotic health. This study investigates the use of adaptive neuro-fuzzy inference system (ANFIS) for ozone prediction. The initial fuzzy inference system is developed by using fuzzy C-means (FCM) and subtractive clustering (SC) algorithms, which determines the important rules, increases generalization capability of the fuzzy inference system, reduces computational needs, and ensures speedy model development. The study area is located in the Empty Quarter of Saudi Arabia, which is considered as a source of huge potential for oil and gas field development. The developed clustering algorithm-based ANFIS model used meteorological data and derived meteorological data, along with NO and NO₂ concentrations and their transformations, as inputs. The root mean square error and Willmott's index of agreement of the FCM- and SC-based ANFIS models are 3.5 ppbv and 0.99, and 8.9 ppbv and 0.95, respectively. Based on the analysis of the performance measures and regression error characteristic curves, it is concluded that the FCM-based ANFIS model outperforms the SC-based ANFIS model.