The arrival of the “double carbon” era indicates that industrial carbon reduction with high energy consumption and high carbon emission is imperative. From the perspective of carbon emission driving factors, industrial carbon emission is decomposed into five influencing factors: energy intensity, energy structure, industrial structure, economic efficiency, and employee scale. Taking the data of 41 subindustries of industrial industry in Liaoning Province from 2010 to 2019 as the research sample, the carbon emission is calculated. The LMDI model is used to analyze and point out the difference in the driving contribution of carbon emissions of each subindustry. The results show that the total carbon emission of Liaoning province gradually decreases, decreases for the first time in 2014, and gradually turns from flat to upward. Economic efficiency is the only and most important reason to drive the increase of industrial carbon emissions in Liaoning Province, and energy efficiency is the primary factor to curb carbon emissions. High carbon industries play a significant role in promoting the increase of carbon emissions, while the medium and low carbon industries have a better effect on restraining carbon emissions. It provides reference and theoretical basis for the government to adjust the industrial structure, control industrial overcapacity, and realize the “double carbon” goal as soon as possible. It is of great significance for the country to optimize energy layout, ensure energy security, and implement the new energy strategy.

The distribution of relief materials is an important part of post-disaster emergency rescue. To meet the needs of the relief materials in the affected areas after a sudden disaster and ensure its smooth progress, an optimized dispatch model for multiple periods and multiple modes of transportation supported by the Internet of Things is established according to the characteristics of relief materials. Through the urgent production of relief materials, market procurement, and the use of inventory collection, the needs of the disaster area are met and the goal of minimizing system response time and total cost is achieved. The model is solved using CPLX software, and numerical simulation and results are analyzed using the example of the COVID-19 in Wuhan City, and the dispatching strategies are given under different disruption scenarios. The results show that the scheduling optimization method can meet the material demand of the disaster area with shorter time and lower cost compared with other methods, and can better cope with the supply interruptions that occur in post-disaster rescue.

As urban populations and economies grow, the amount of municipal solid waste is expected to increase. To address this, waste sorting and collection have become crucial for implementing effective waste classification policies and promoting smart city development. Given the uncertainties in waste volume and the environmental impact of wet waste transportation, this paper presents optimization models for both dry and wet garbage vehicle routing. The goal is to minimize total costs, carbon footprint, and secondary pollution. A random chance constraint is introduced based on decision-makers' preferences, and stochastic theory is used to convert this constraint into a probability density equivalent, improving solution efficiency. To tackle the complexity of waste collection and transportation, an improved genetic algorithm (GA-LS) combining genetic algorithms and a two-layer local search method is proposed. This enhances the exploration ability of the solution space. The model is validated through actual case studies and sensitivity analysis, demonstrating that it can generate near-optimal solutions within an acceptable time frame. The optimization reduces costs, minimizes environmental pollution, and improves residents' satisfaction. Furthermore, it provides a theoretical basis for determining the final wet waste treatment site and optimal transportation routes. Future research will integrate generative deep learning models to improve waste volume prediction accuracy, supporting more efficient and cost-effective smart city decisions.

The idea of a “green port” is becoming increasingly popular worldwide in the seaport industry, indicating a heightened dedication to environmental sustainability. However, there is a growing concern among the international community regarding the expansion of port facilities and the environmental impact of emissions from shipping. Thus, as the primary driver of climate change, CO2 emissions must be carefully controlled. African ports have had difficulties implementing the greening of ports project due to economic, technological, socio-cultural, legal, topographical, and fiscal constraints. To counteract the effects of climate change, several port authorities have implemented tactics related to the greening project, which is a smart concept. Concerning African ports’ current condition ‒ namely, their deficiency in port infrastructure ‒ is this proposal beneficial or detrimental? In this context, this paper has employed the GM (1, 1) grey forecasting model to predict the future carbon dioxide (CO2) emissions of the port from 2023 to 2040, using Mombasa Port as a case study and as one of the African ports that have implemented green port initiatives. The aim is to compare the “before” and “after” implementation conditions of green port policies in the Mombasa Port, utilizing the port’s CO2 emissions from 2009 to 2022 as the available data. Therefore, the data was separated into two sets: “before implementation”, i.e., from 2009-2015, and “after implementation”, i.e., from 2016-2015. The average relative error of the GM (1, 1) for both “before implementation” and “after implementation”, based on the study’s CO2 emission forecasts, is less than 10%. For the next 18 years, the GM (1, 1) model predicted continued increases in CO2 emissions based on the data from “before implementation”, whereas the data from “after implementation” showed a decrease in CO2 emissions. This observation confirmed our initial assumption that, based on data from the years 2016-2022, the CO2 emission forecast value for 2040 must be lower than that of 2040 based on data from the years 2009-2022. Furthermore, this paper also highlighted some key achievements realized through the successful implementation of the Green Port Policy program by the Kenya Ports Authority at Mombasa Port. By improving and modernizing Mombasa Port, this initiative also marks the beginning of a new era in Africa that is focused on environmental sustainability.