Nature-based solutions are critical for putting Brazil on track towards net-zero emissions by 2050.

Most of the world's nations (around 130) have committed to reaching net-zero carbon dioxide or greenhouse gas (GHG) emissions by 2050, yet robust policies rarely underpin these ambitions. To investigate whether existing and expected national policies will allow Brazil to meet its net-zero GHG emissions pledge by 2050, we applied a detailed regional integrated assessment modelling approach. This included quantifying the role of nature-based solutions, such as the protection and restoration of ecosystems, and engineered solutions, such as bioenergy with carbon capture and storage. Our results highlight ecosystem protection as the most critical cost-effective climate mitigation measure for Brazil, whereas relying heavily on costly and not-mature-yet engineered solutions will jeopardise Brazil's chances of achieving its net-zero pledge by mid-century. We show that the full implementation of Brazil's Forest Code (FC), a key policy for emission reduction in Brazil, would be enough for the country to achieve its short-term climate targets up to 2030. However, it would reduce the gap to net-zero GHG emissions by 38% by 2050. The FC, combined with zero legal deforestation and additional large-scale ecosystem restoration, would reduce this gap by 62% by mid-century, keeping Brazil on a clear path towards net-zero GHG emissions by around 2040. While some level of deployment of negative emissions technologies will be needed for Brazil to achieve and sustain its net-zero pledge, we show that the more mitigation measures from the land-use sector, the less costly engineered solutions from the energy sector will be required. Our analysis underlines the urgent need for Brazil to go beyond existing policies to help fight climate emergency, to align its short- and long-term climate targets, and to build climate resilience while curbing biodiversity loss.

The q-G method : A q-version of the Steepest Descent method for global optimization.

In this work, the q-Gradient (q-G) method, a q-version of the Steepest Descent method, is presented. The main idea behind the q-G method is the use of the negative of the q-gradient vector of the objective function as the search direction. The q-gradient vector, or simply the q-gradient, is a generalization of the classical gradient vector based on the concept of Jackson's derivative from the q-calculus. Its use provides the algorithm an effective mechanism for escaping from local minima. The q-G method reduces to the Steepest Descent method when the parameter q tends to 1. The algorithm has three free parameters and it is implemented so that the search process gradually shifts from global exploration in the beginning to local exploitation in the end. We evaluated the q-G method on 34 test functions, and compared its performance with 34 optimization algorithms, including derivative-free algorithms and the Steepest Descent method. Our results show that the q-G method is competitive and has a great potential for solving multimodal optimization problems.