Water quality constrained adjustment planning for regional breeding management with nonlinear programming model under uncertainty in Wenchang City, China.

Basin water pollution caused by livestock, poultry and fish breeding is still a serious problem for remote villages, however, reliable regional breeding management programming have the potentials to improve pollution status. This paper focuses on the optimal model design and water quality analysis of the livestock, poultry and fish breeding system for Wenchang City, China. Methods of multi-objective programming (MOP), interval parameter programming (IPP), fuzzy-stochastic parameter programming (FSPP), and chance constrained programming (CCP) were incorporated into the developed model to tackle multi uncertainties described by interval values, probability distributions, fuzzy membership function. Based on the estimation of local breeding potential and current situation of surface water section, a multi-objective mixed fuzzy-stochastic nonlinear programming optimization model is presented with one-dimensional water quality model. In order to evaluate the environmental carrying capacity of livestock, poultry and fishery manure, predict its development trend and investigate the implementation effect of different emission reduction policies, this paper designs quantization system of the urban water environmental carrying capacity for the model. The results indicated that the water environment pollutant absorption capacity and carrying capacity of Wenchang city have approached the limit especially the towns in the northeast of City which limited the overall development space of the City. The modeling results are valuable for supporting the adjustment of the existing livestock, poultry and fish breeding schemes within a complicated system benefit and surface water quality situation under uncertainty.

Multi-objective optimisation model of a low-cost path to peaking carbon dioxide emissions and carbon neutrality in China.

A low-cost path system for achieving carbon neutrality in China was modelled using multi-objective programming by integrating industrial production, electric power, heating, transportation, and forest carbon sequestration. We aimed to minimise the total system cost, CO2 emissions, and air pollutants. The constraints included China's targets of peaking CO2 emissions before 2030; achieving carbon neutrality before 2060; ensuring industry, power, heating, and transportation supplies; promoting green energy; and implementing emission control. The model accounted for industries with high coal consumption, such as steel and chemical industries. Various power sources were considered, including coal-fired, nuclear, wind, and solar energy. Forest carbon sink and carbon capture and storage technologies were employed to achieve the emission reduction goals. The model, which was validated using available research data, offered cost-effective path schemes and exhibited high validity. Our findings emphasise the importance of structural adjustments and emission control, with electric power, heating, and transportation sectors showing higher feasibility and providing greater contributions to achieving carbon neutrality than other industries. Conversely, industrial transformation in sectors such as iron and steel, chemical, and construction materials had low feasibility and limited contribution. The modelling outcomes provide valuable insights for developing low-cost, carbon emission-targeted transportation structures in China's complex system. The results presented here demonstrate the global applicability of this method in contributing to plans aimed at meeting key carbon reduction targets.

Measurement of Tourism-Related CO2 Emission and the Factors Influencing Low-Carbon Behavior of Tourists: Evidence from Protected Areas in China.

In the fight against climate change, future policy directions in the transition toward a green travel- and tourism-based economy include improving tourism-derived CO2 emission levels and guiding individual low-carbon behavior. In China, people tend to engage in outdoor adventure travel and cultural tourism in natural areas. However, limited information is available on the empirical evaluation of energy use and the CO2 emissions associated with tourism in protected areas. The present study used a life cycle assessment to explore energy use and CO2 emissions due to tourism and identify the factors driving low-carbon behavior. To these ends, survey data for the protected areas of the Qinling Mountains from 2014 to 2019 were used. The results showed that energy use and CO2 emissions in various tourism sectors steadily increased from 2014 to 2019, primarily because of an increase in transportation activity. This study used data derived from the calculation of CO2 emissions per tourist per trip to identify the various factors jointly contributing to the low-carbon behavior of tourists. These included a low-carbon attitude, low-carbon knowledge, environmental education, and policy reward. The broader implications of this study are that several emission reduction policy options are available to address the challenges inherent in sustainable tourism development and that these policies may be selected according to specific conditions. The low-carbon transformation of recreational facilities at travel destinations, policy rewards, and environmental education can regulate tourist behavior, holding the key to sustainable tourism development in protected areas.

A comparative study of stage characteristics and factorial decomposition of CO2 emissions between China and the USA.

CO2 contributes a lot to the greenhouse effect. The total CO2 emissions of the two countries, China and the USA, as the world's top two economies, have exceeded 40% of the total global carbon emissions. In this context, the exploration of the evolution of carbon emissions from energy consumption in China and the USA and the comparison of the characteristics of carbon emission drivers in different periods play a significant role in the policy formulation and climate change cooperation between China and the USA. In this study, the BP structural breakpoint test was used to divide the carbon emission stages of China and the USA from 1970 to 2019. The generalized Divisia index model (GDIM) was developed to decompose the growth of carbon emissions in China and the USA into eight items, GDP, carbon intensity of GDP, energy use, carbon intensity of energy, population, carbon emissions per capita, GDP per capita, and energy intensity, and to analyze the characteristics and cumulative contribution of carbon emission drivers at each stage. Based on the stage and cumulative characteristics of carbon emissions between China and the USA, the USA should take the initiative to assume the legal responsibility of carbon emissions and further deepen the cooperation with other countries in the field of climate change. China should transform the economic growth mode, optimize the energy structure, and improve the efficiency of resource utilization to help achieve the peaking carbon emissions and the carbon neutrality smoothly.