Economy-carbon coordination in integrated energy systems: Optimal dispatch and sensitivity analysis

Innovative measurement, trade-off-synergy relationship and influencing factors for agricultural net carbon emissions and effective supply of agricultural products in China

“…The control variables include urbanization level, financial support for agriculture, agricultural production conditions, financial environmental protection expenditure, research and development investment and the degree of openness, and are used to compensate for the neglect or abandonment of other factors and related to the explained variable [ 31 , 44 ]. Here, in order to ensure the significance of variable selection and entire study, we conduct a sensitivity analysis based on Lu et al (2023) [ 45 ] and converted the sensitivity to a range of 0–1, in which 0 represents insensitivity whereas 1 representing extreme sensitivity, and the larger the value, the greater the relationship between the variables will be. The results reveal that the sensitivities of green technology innovation to ANCE and ESAP are respectively 0.892 and 0.755, while the average sensitivities of control variables to ANCE and ESAP are respectively 0.562 and 0.886.…”

Sustainable Waste Management in Malaysia: Leveraging Supply Chain Solutions for a Greener Future

“…Supply chain principles can be applied to improve waste collection and segregation, essential steps in the waste-to-energy process. Scholars like Xu et al (2020) highlight that efficient collection systems, using innovative technology and optimized routing, can enhance waste pick-up and minimize transportation costs. Moreover, adequate waste segregation at the source facilitates the separation of organic waste from non-recyclables, improving feedstock quality for waste-to-energy conversion (He et al, 2019).…”