This paper proposes a linearly implicit, structure-preserving scheme for the generalized Zakharov system. For the construction of the numerical scheme, we first rewrite the system into an equivalent first-order formulation. We then construct a linearly implicit scheme based on the leap-frog method, together with a suitable starting procedure. We prove that the proposed scheme preserves both mass and energy in the discrete sense. Numerical experiments are carried out to validate the theoretical results.
Carbon capture and storage (CCS) is critical for decarbonizing the fossil-energy power sector, the world's largest source of greenhouse gas emissions. However, how deployment responsibilities should be allocated across heterogeneous regions to deliver economy-wide economic and environmental benefits remains poorly understood. This study develops an integrated optimization–multi-regional input-output–computable general equilibrium (OP-MRIO-CGE) framework to evaluate the economic outcomes and emissions reduction effects of alternative regional CCS deployment policies, emphasizing cross-regional spillovers and implications for policy implementation. Using China as a case study, results show that outcomes are highly sensitive to regional responsibility allocation. Scenario 6 formulates deployment as a multi-objective problem maximizing decarbonized fossil-energy power generation and minimizing total CCS deployment cost. The objectives are evaluated separately through Pareto dominance, without normalization or objective weights, producing non-dominated allocations rather than a unique optimum. One allocation is randomly selected as the reference solution before its downstream MRIO-CGE outcomes are assessed, and sensitivity analysis covers the entire retained Pareto set. This reference solution reduces economy-wide CO2 emissions by approximately 0.379 billion tonnes in 2030, increases national GDP by approximately USD 0.14 trillion, and improves consumer welfare by approximately USD 610 million. Concentrated CCS deployment in the Bohai Rim and the Junggar Basin increases emissions in non-host regions, including Sichuan, Hubei, and Anhui, while economic output losses occur in Shaanxi, Guangdong, and Jiangsu. These findings provide a quantitative basis for coordinated multi-regional CCS deployment and targeted complementary policies, offering an analytical framework applicable to other large, spatially heterogeneous economies pursuing deep decarbonization.
Actuator-rate-sensitive applications require feedback laws that discourage abrupt changes in the control input. For unknown discrete-time linear systems, this paper develops an input-increment-aware off-policy Q-learning algorithm that learns the optimal rate-aware feedback from input-state data. The previous input is treated as part of an augmented state and the input increment as the new action. This restores Markovianity but yields a linear quadratic problem with a persistent state-action cross term, so the usual cross-term-free linear-quadratic-regulator Q-learning update is not directly applicable. We establish structural validity of the augmented problem, derive a cross-coupled off-policy regression and policy-improvement rule, and prove admissibility preservation, monotone value improvement, convergence to the optimal incremental gain, and joint convergence of the plant state and the control signal. The increment weight is further shown to provide an a-posteriori rate certificate and a certified structural lower bound on the conditioning of the policy-improvement step under regression error. Numerical tests on academic plants and a quarter-car active suspension benchmark confirm the predicted smoothing and robustness effects, and benchmark the learned closed loop against a rate-constrained model predictive control oracle that has full plant knowledge and road-disturbance preview.
The Ganges Delta, largely situated in Bangladesh, is one of the world’s most densely populated and climate-exposed regions, where environmental vulnerability intersects with persistent poverty. Although national poverty trends are well documented, systematic evidence from designated climate hotspots remains limited. We address this knowledge gap by examining poverty trends and factors associated with poverty across seven climatic hotspots identified in the Bangladesh Delta Plan 2100, using 93,601 repeated cross-sectional household observations from 1995 to 2019. Results show a substantial decline in poverty across all hotspots over the past two decades; however, progress has been spatially uneven. Persistent deprivation remains pronounced in the Barind and drought-prone areas, followed by relatively less hazard-prone (RLHP), estuarine, coastal, haor and flood, hill, and urban regions. These disparities are associated with environmental stress, limited livelihood diversification, infrastructural deficits, restricted service access, and weak social capital. Extreme poverty in several hotspots remains both persistent and volatile, indicating continued structural vulnerability despite aggregate improvements. A dynamic within-cohort fixed-effects pseudo-panel regression reveals that human capital accumulation, economic transformation, improved living standards, digital access, gender equity, and social capital significantly reduce poverty risk, whereas adverse health conditions, demographic pressures, and exposure to climatic shocks exacerbate it. Across hotspots, we identify a structural climate–poverty paradox: environmental stressors heighten consumption volatility and erode welfare gains even amid economic progress. Robustness checks using a nonparametric two-stage copula control function to address endogeneity corroborate these findings. Overall, the study underscores the need for spatially differentiated, climate-responsive policies rather than centralized poverty reduction strategies.
Existing research on students’ choices of higher education majors often emphasizes factors such as salary expectations, family background, and personal interests. However, studies exploring the impact of public crises, particularly the COVID-19 pandemic, on students’ academic preferences remain limited and inconclusive. This study aims to examine how the exogenous shock of the COVID-19 pandemic has influenced Chinese high school students’ preferences for public health and preventive medicine majors. We analyze admissions panel data from 128 universities across 22 provinces (2017–2021) using a high‑dimensional fixed effects (HDFE) model. The findings reveal that the pandemic significantly reduced the competitiveness of admissions into public health majors. Heterogeneity emerges across university types: “Double First‑Class” institutions show limited sensitivity to pandemic‑related risk, whereas general undergraduate universities experience a marked decline in applications. Salary premium, government transparency, and social altruism exert distinct moderating effects. Specifically, salary competitiveness and altruistic support mitigate the negative impact of COVID-19, whereas greater government transparency amplifies risk perceptions and further discourages applications. Overall, the results underscore the need for targeted education policies to enhance the attractiveness of public health professions—strengthening system resilience—and offer practical guidance for strategic enrollment management and program design in the face of crisis‑driven demand shocks.
