As the world's biggest carbon dioxide (CO₂) emitter and the largest developing country, China faces daunting challenges to peak its emissions before 2030 and achieve carbon neutrality within 40 years. This study fully considered the carbon-neutrality goal and the temperature rise constraints required by the Paris Agreement, by developing six long-term development scenarios, and conducting a quantitative evaluation on the carbon emissions pathways, energy transformation, technology, policy and investment demand for each scenario. This study combined both bottom-up and top-down methodologies, including simulations and analyses of energy consumption of end-use and power sectors (bottom-up), as well as scenario analysis, investment demand and technology evaluation at the macro level (top-down). This study demonstrates that achieving carbon neutrality before 2060 translates to significant efforts and overwhelming challenges for China. To comply with the target, a high rate of an average annual reduction of CO₂ emissions by 9.3% from 2030 to 2050 is a necessity, which requires a huge investment demand. For example, in the 1.5 °C scenario, an investment in energy infrastructure alone equivalent to 2.6% of that year's GDP will be necessary. The technological pathway towards carbon neutrality will rely highly on both conventional emission reduction technologies and breakthrough technologies. China needs to balance a long-term development strategy of lower greenhouse gas emissions that meets both the Paris Agreement and the long-term goals for domestic economic and social development, with a phased implementation for both its five-year and long-term plans.

Three major elements, carbon, hydrogen, and nitrogen, in twenty-four bituminous coal samples, were measured by laser-induced breakdown spectroscopy. Argon and helium were applied as ambient gas to enhance the signals and eliminate the interference of nitrogen from surrounding air. The relative standard deviation of the related emission lines and the performance in the partial least squares (PLS) modeling were compared for different ambient environments. The results showed that argon not only improved the intensity, but also reduced signal fluctuation. The PLS model also had the optimal performance in multi-element analysis using argon as ambient gas. The root mean square error of prediction of carbon concentration decreased from 4.25% in air to 3.49% in argon, while the average relative error reduced from 4.96% to 2.98%. Hydrogen line demonstrated similar improvement. Yet, the nitrogen lines were too weak to be detected even in an argon environment which suggested the nitrogen signal measured in air come from the breakdown of nitrogen molecules in the atmosphere.

Malaysia is a rapidly developing country in Southeast Asia that aims to achieve high-income country status by 2020; its economic growth is highly dependent on its abundant energy resources, especially natural gas and crude oil. In this paper, a complete picture of Malaysia’s energy use from primary source to end use is presented by mapping a Sankey diagram of Malaysia’s energy flows, together with ongoing trends analysis of the main factors influencing the energy flows. The results indicate that Malaysia’s energy use depends heavily on fossil fuels, including oil, gas and coal. In the past 30 years, Malaysia has successfully diversified its energy structure by introducing more natural gas and coal into its power generation. To sustainably feed the rapidly growing energy demand in end-use sectors with the challenge of global climate change, Malaysia must pay more attention to the development of renewable energy, green technology and energy conservation in the future.

Artificial intelligence (AI) is increasingly essential for optimizing energy systems, addressing the growing complexity of energy management, and supporting the integration of diverse renewable sources. This study systematically reviews AI-enabled modeling approaches, highlighting their applications, limitations, and potential in advancing sustainable energy systems while offering insights and a framework for addressing real-world energy challenges. Data-driven models excel in energy demand prediction and resource optimization but face criticism for their “black-box” nature, while mechanism-driven models provide deeper system insights but require significant computation and domain expertise. To bridge the gap between these approaches, hybrid models combine the strengths of both, improving prediction accuracy, adaptability, and overall system optimization. This study discusses the policy background, modeling approaches, and key challenges in AI-enabled energy system modeling. Furthermore, this study highlights how AI-enabled techniques are paving the way for future energy system modeling, including integration and optimization for renewable energy systems, real-time optimization and predictive maintenance through digital twins, advanced demand-side management for optimal energy use, and hybrid simulation of energy markets and business behavior.