The impact of the climate and environmental problems experienced in the world with the Industrial Revolution has prominently begun to be felt today, and the consequences of climate change on the environment and public health have now become visible. The increase in greenhouse gas emissions resulting from human activities, which is the main cause of global climate change, caused the global surface temperature to be 1.1 °C higher between 2011 and 2020 compared to 1850–1900. In parallel with this global problem, the transition to clean energy has increased significantly with Russia's invasion of Ukraine, more aggressive energy and climate policies, technological developments, and increasing concerns about energy security. In this study, global climate change indicators, including land and sea surface air temperatures, sea level rise, sea ice extent, ocean heat content, surface humidity, and total column water vapor, are reviewed and updated in parallel with a comprehensive analysis of the progress in renewable energy. The results showed that if no measures are taken to reduce human-induced greenhouse gas emissions, the global average temperature will increase further in the coming years and the negative effects of other climate parameters will be felt even more. It has been emphasized that limiting human-induced global warming requires renewable and sustainable energy sources and net zero CO₂ emissions and that the simultaneous adoption of emission reduction and adaptation strategies will be the most effective economic and technical solution to the global warming problem.

Wind energy, which generates zero emissions, is an environmentally friendly alternative to conventional electricity generation. For this reason, wind energy is a very popular topic, and there are many studies on this subject. Previous studies have often focused on onshore or offshore installations, lacking comprehensive comparisons and often not accounting for technological advancements and their impact on cost and efficiency. This study addresses these gaps by comparing onshore and offshore wind turbines worldwide in terms of installed capacity, levelized cost of electricity (LCOE), total installed cost (TIC), capacity factor (CF), turbine capacity, hub height, and rotor diameter. Results show that onshore wind power capacity constituted 98.49% in 2010, 97.23% in 2015, and 92.9% in 2022 of the world’s total cumulative installed wind power capacity. Offshore wind capacity has increased yearly due to advantages like stronger, more stable winds and easier installation of large turbine components. LCOE for onshore wind farms decreased from 0.1021 USD/kWh in 2010 to 0.0331 USD/kWh in 2021, while offshore LCOE decreased from 0.1879 USD/kWh in 2010 to 0.0752 USD/kWh in 2021. By 2050, wind energy will contribute to 35% of the global electricity production. This study overcomes previous limitations by providing a comprehensive and updated comparison that incorporates recent technological advancements and market trends to better inform future energy policies and investments.

In recent decades, climate change has emerged as a significant issue, causing ongoing increases in ocean and atmospheric temperatures. This trend indicates that sea levels are expected to rise at faster rates in the future compared to the present sea level. Ongoing increases in sea levels could potentially trigger catastrophic natural disasters worldwide. So that reason, predicting sea level rise (SLR) is crucial for future planning in areas such as human living conditions, flood prevention, and coastal development. This study focuses on evaluating the ability of conventional and deep learning time series methods such as seasonal autoregressive integrated moving average (SARIMA), long short-term memory (LSTM) neural network, and gated recurrent unit (GRU) in estimating the current and future global mean SLR. The models were trained and tested using monthly SLR data collected between 1993 and 2023 and then future predictions were made until 2050. A total of 366 monthly SLR data were used where 288 SLR data (78%) from January 1993 to December 2016 were utilized in the training phase and 78 SLR data (22%) from January 2017 to June 2023 were utilized in the testing phase. The findings demonstrate that although the SLR values estimated with the all developed models are closely compromised with real SLR values in the testing phase, the LSTM model provides more precise predictions than the others. While MAPE, MAE and RMSE parameters for the prediction of SLR data with the LSTM model are observed as 0.0631%, 0.0058 cm and 0.0073 cm, respectively, they are detected as 0.0899%, 0.0084 cm and 0.0109 cm with GRU model. On the other hand, in the testing stage, the worst estimation is performed with the SARIMA model with a MAPE of 0.1335%, MAE of 0.0123 cm and RMSE of 0.0155 cm. Furthermore, the LSTM algorithm which is observed as the best accurate model, predicts the SLR values as 17.218 cm by July 2040 and 21.236 cm by July 2050 when the global average sea level at the beginning of 1993 is taken as reference. Therefore, the developed algorithms show potential as effective tools for modeling and predicting global mean sea level rise in the future and this study will be highly valuable for decision-makers in creating mitigation strategies for sea level rise associated with climate change, utilizing the developed models.