中国“碳中和”目标的实现路径与模式选择

围绕中国碳中和目标的实现路径与模式选择问题，提出了一个中国应对气候变化整体治理模式，即多阶段混合主导型碳中和实现模式。这一模式是以国家为核心，在一定时期和内外环境约束下，通过减排和增汇路径，对中国境内由人类活动造成的CO2排放与人为CO2吸收量之间关系进行调整和平衡的过程；其内部由宏观、中观和微观三层结构组成，每层结构各不相同。多阶段混合主导型碳中和实现模式的提出，拓展和完善了气候变化整体治理理论，为气候变化治理提供了机制借鉴。     

Linear scalar-on-surface random effects regression models

Many research fields increasingly involve analyzing data of a complex structure. Models investigating the dependence of a response on a predictor have moved beyond the ordinary scalar-on-vector regression. We propose a regression model for a scalar response and a surface (or a bivariate function) predictor. The predictor has a random component and the regression model falls in the framework of linear random effects models. We estimate the model parameters via maximizing the log-likelihood with the ECME (Expectation/Conditional Maximization Either) algorithm. We use the approach to analyze a data set where the response is the neuroticism score and the predictor is the resting-state brain function image. In the simulations we tried, the approach has better performance than two other approaches, a functional principal component regression approach and a smooth scalar-on-image regression approach.     

Noninformative priors for the between-group variance in the unbalanced one-way random effects model with heterogeneous error variances

For the unbalanced one-way random effects model with heterogeneous error variances, we propose the non-informative priors for the between-group variance and develop the first- and second-order matching priors. It turns out that the second-order matching priors do not exist and the reference prior and Jeffreys prior do not satisfy a first-order matching criterion. We also show that the first-order matching prior meets the frequentist target coverage probabilities much better than the Jeffreys prior and reference prior through simulation study, and the Bayesian credible intervals based on the matching prior and reference prior give shorter intervals than the existing confidence intervals by examples.     

森林可持续经营的政策支持体系——亚太区域示范林项目中国临安示范林实践

运用参与性诊断(即PRRA)方法对临安示范林地区的森林经营主要政策进行了诊断分析,找出了现行政策存在的问题,如:政策环境欠佳,产权政策不到位,税费政策不合理,林业产品生产、加工和销售脱节,森林生态效益补偿政策欠规范、不合理等.同时,运用参与性设计的方法对若干主要政策进行专项设计,并在此基础上,从优化森林可持续经营的政策环境和规范政策系统运行两方面入手,提出一套科学、合理和有效的适合当地水平的森林可持续经营综合设计方案,以促进示范林网络的发展.     

对林业税费的经济学分析

该文运用经济学的基本工具———供给和需求曲线 ,以商品林为研究对象 ,探讨了林业税费的作用机理和内涵。该文认为商品林生产具有正的外部性 ,市场失灵使生产者将不能提供社会真正所需要的商品林数量。在封闭的市场条件下 ,林业税费会直接成为价格的一部分 ,商品林生产者应当能够获得正常利润 ,与税费高低无关 ,但会使一部分林地退出生产。林业补贴是一种负的林业税收。在开放市场经济条件下 ,国内商品林供应量低于没有林业税收时的商品林供应量 ,更低于考虑社会生态需求时的商品林供应量 ,造成巨大的社会福利损失 ,应减免林业税费、增加林业扶持、取消价格管制。该文进一步研究了与林业税费密切相关的森林采伐限额政策 ,认为对商品林应该完全取消采伐限额政策     

森林旅游业实施绿色营销的研究

深刻分析了传统市场营销策略对森林旅游业所产生的负面影响。阐述了森林旅游业实施绿色营销的基本原则 ,并提出相应的实施对策。这些对策主要包括努力塑造绿色企业形象 ,建立新型的绿色管理模式 ,大力开发森林旅游绿色产品和生态道德建设     

指数级数的增长性和值分布

本文研究了随机狄里克莱级数 在随机变量序列{Xn}独立（可不同分布）以及满足等条件时的增长性以及值分布,得到了一些新的结果.     

一种违法性认识理论的新主张及其在涉林案件犯罪故意认定中的具体运用

以社会危害性认识为参照,将我国学者关于违法性认识在犯罪故意中的地位的诸多观点归纳为单一说、兼容说和主次说三类。在批评和借鉴上述观点的基础上,提出了"社会危害性认识为主,违法性认识可能性为辅说"的新主张,并对其在涉林案件犯罪故意认定中的具体运用进行了讨论。     

A Bayesian Adjustment for Covariate Misclassification with Correlated Binary Outcome Data

Estimated associations between an outcome variable and misclassified covariates tend to be biased when the methods of estimation that ignore the classification error are applied. Available methods to account for misclassification often require the use of a validation sample (i.e. a gold standard). In practice, however, such a gold standard may be unavailable or impractical. We propose a Bayesian approach to adjust for misclassification in a binary covariate in the random effect logistic model when a gold standard is not available. This Markov Chain Monte Carlo (MCMC) approach uses two imperfect measures of a dichotomous exposure under the assumptions of conditional independence and non-differential misclassification. A simulated numerical example and a real clinical example are given to illustrate the proposed approach. Our results suggest that the estimated log odds of inpatient care and the corresponding standard deviation are much larger in our proposed method compared with the models ignoring misclassification. Ignoring misclassification produces downwardly biased estimates and underestimate uncertainty.