This paper presents a comprehensive review of building lifecycle carbon emissions (CEBL) and reduction approaches, analyzing over 300 recent publications and engaging in in-depth discussion of more than 100 key studies. The review systematically examines CO₂ emissions across all stages of a building's lifecycle, from material production and transportation to construction, operation, demolition, and material recycling. While existing research highlights the significance of operational energy efficiency, this review reveals critical research gaps in quantifying transportation emissions, accounting for on-site equipment emissions during construction, and addressing the unique characteristics of non-residential buildings. Furthermore, the paper underscores the urgent need for improved construction waste management practices, especially in developing countries where landfilling remains prevalent. For the building carbon reduction approaches, promising building carbon emission reduction approaches include leveraging carbon dioxide sinks, implementing integrated energy systems, integrating building-integrated photovoltaics (BIPV), and enacting effective policy interventions are separately discussed. By revealing data and theoretical limitations within current research, this review calls for more integrated and context-specific approaches to CEBL assessment, paving the way for a more sustainable built environment.

The energy harvested by an ocean wave energy converter (WEC) can be enhanced by a well-designed wave-by-wave control strategy. One of such superior control methods is model predictive control (MPC), which is a nonlinear constrained optimization control strategy. A limitation of the classical MPC algorithm is its requirement of an accurate WEC dynamic model for real-time implementation. This article overcomes this challenge by proposing a data-driven MPC scheme for wave energy converters. The data-based WEC model is developed by a Gaussian process (encompassing mean predictions and symmetric uncertainties) for a more accurate description of nonlinear and unmodeled system dynamics. A cross-entropy solver for data-driven MPC is employed for rapid, high-performance results, which samples trajectories from Gaussian distributions based on the concept of the symmetry principle. The proposed strategy is verified numerically by simulations which demonstrate its superior performance over a classical complex-conjugate controller.