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The paper aims at investigation of the nexus between Industry 4.0 (I4.0) and environmental sustainability in addition to exploring the long-run and short-run effects of Industry 4.0 on CO 2 emissions. For this end, energy consumption, internet and communication technology (ICT) exports, research and development (R&D), artificial intelligence (AI), ICT technology patents and bitcoin are taken as control variables of Industry 4.0 for a panel of 9 countries that contribute to 62% of the total CO 2 emissions in the world. For this purpose, the paper follows two approaches. First, the paper proposes utilization of AI and ICT technology patents, technology-related R&Ds and ICT exports as variables of I4.0 in addition to investigating the effects of economic growth, energy consumption and bitcoin. Second, to control structural changes and nonlinearity in the cointegrating relations and existence of degenerate cointegration, Fourier panel bootstrap autoregressive distributed lag model (FPBARDL) is utilized. Afterwards, FPBARDL based long, short and strong causality analyses are conducted. The empirical findings revealed positive impacts of all I4.0-related variables on emissions in the long-run. Though I4.0 related AI and ICT innovation has no direct effect in the short-run, its effects are determined through increased energy consumption towards emissions. The strong positive effects of energy consumption and positive effects of economic growth, ICT exports and R&D are observed both in the short and long-run. In addition to positive impacts of I4.0 on environment, the findings favor insufficiency of policies focusing on lowering emissions in I4.0 context.Policy recommendations include strong commitment to energy efficiency and renewable energies and trade policies with environmental concerns. [ABSTRACT FROM AUTHOR]

With billion tons of carbon dioxide emitted by the construction and manufacturing sector every year, there is an urgent need for reducing construction materials consumption as the construction industry continues to grow. Current practices for designing structural elements, which are based on traditional design methods, result in building box-shaped elements and consuming more materials than what is structurally necessary. Excess materials can be removed without compromising the structural soundness of the elements while also providing added architectural value. This paper presents an approach based on generative design concepts, genetic algorithms, and the Lagrangian Multiplier Method to design concrete beams that use near-exact amounts of material only where they are needed. In this study, beam designs are optimized to reduce concrete and steel use in reinforced concrete beams. The effectiveness of the approach was demonstrated through its deployment to design a cantilever beam and a simply supported beam. The results of both applications were very promising as the reductions in material costs reached 63% and the related reductions in CO 2 emissions reached 57% per beam. This lays strong foundations for expanding the effort to scale this approach into entire buildings. The optimized structures can be constructed using 3D concrete printing technology, which eliminates the barriers imposed by traditional form-based construction. The results of this study contribute to means for making construction more economical and environmentally sustainable. • Traditional design methods of structural concrete elements result in material waste. • Cement production significantly contributes to the earth's greenhouse emissions. • Applying generative design concepts to concrete structures reduces material waste. • Resulting cost reductions could reach 63% per simply supported or cantilever beam. • The corresponding CO 2 emission reductions could reach 57% per beam. [ABSTRACT FROM AUTHOR]