How to achieve the 2030 CO2 emission-reduction targets for China's industrial sector: Retrospective decomposition and prospective trajectories

The Chinese government actively follows the low-carbon development pattern and has set the definite targets of reducing carbon emissions by 2030. The industrial sector plays a significant role in China's economic growth and CO₂ emissions. This is the first study to present a specific investigation on the retrospective decomposition (1993–2014) and prospective trajectories (2015–2035) of China's industrial CO₂ emission intensity (ICEI) and industrial CO₂ emissions (ICE), aiming at China Industrial Green Development Plan 2016–2020 targets and China's 2030 CO₂ emission-reduction targets. We introduce process carbon intensity, investment and R&D factors into the decomposition model and make a combination of dynamic Monte Carlo simulation and scenario analysis to identify whether and how the targets would be realized from a sector-specific perspective. The results indicate that investment intensity is the primary driver for the increase in ICEI, while R&D intensity and energy intensity are the leading contributors to the reduction in ICEI. Under existing policies, it is very possible for the industrial sector to achieve the 2020 and 2030 intensity-reduction targets. However, the realization of 2030 emission-peak target has some uncertainties and needs extra efforts in efficiency improvement and structural adjustment. All the five scenarios would achieve the 2020 and 2030 intensity-reduction targets, except Scenario N4 for China Industrial Green Development Plan 2016–2020 target. Nonetheless, only three scenarios would realize the 2030 emission-peak target. With strong efficiency improvement and structural adjustment, ICE would hit the peak in 2025. In contrast, with high/low efficiency improvement and weak structural adjustment, ICE would fail to reach the peak before 2035. Both ICEI and ICE have substantial mitigation potentials with the enhancement of efficiency improvement and structural adjustment. Finally, we suggest that the Chinese government should raise the baseline requirements of efficiency improvement and structural adjustment for the industrial sector to achieve China’s 2030 targets.     

The global impacts of US climate policy: a model simulation using GCAM-TU and MAGICC

To assess the potential impacts of the US withdrawal from the Paris Agreement, this study applied GCAM-TU (an updated version of the Global Change Assessment Model) to simulate global and regional emission pathways of energy-related CO₂, which show that US emissions in 2100 would reduce to ?2.4?Gt, ?0.7?Gt and ?0.2?Gt under scenarios of RCP2.6, RCP3.7 and RCP4.5, respectively. Two unfavourable policy scenarios were designed, assuming a temporary delay and a complete stop for US mitigation actions after 2015. Simulations by the Model for the Assessment of Greenhouse-gas Induced Climate Change (MAGICC) indicate that the temperature increase by 2100 would rise by 0.081°C–0.161°C compared to the three original RCPs (Representative Concentration Pathways) if US emissions were kept at their 2015 levels until 2100. The probability of staying below 2°C would decrease by 6–9% even if the US resumes mitigation efforts for achieving its Nationally Determined Contribution (NDC) target after 2025. It is estimated by GCAM-TU that, without US participation, increased reduction efforts are required for the rest of the world, including developing countries, in order to achieve the 2°C goal, resulting in 18% higher global cumulative mitigation costs from 2015 to 2100.

Key policy insights

• President Trump’s climate policies, including planned withdrawal from the Paris Agreement, cast a shadow on international climate actions, and would lower the likelihood of achieving the 2°C target.

• To meet the 2°C target without the US means increased reduction efforts and mitigation costs for the rest of the world, and considerable economic burdens for major developing areas.

• Active state-, city- and enterprise-level powers should be supported to keep the emission reduction gap from further widening even with reduced mitigation efforts from the US federal government.

     

基于灰靶模型的宜兴市农村居民点布局适宜性评价及分类调控研究

对农村居民点布局适宜性评价方法的探讨,不仅能丰富农村居民点布局研究的理论体系,也有利于开展农村居民点规划和整理工作。以江苏省宜兴市为例,综合考虑其社会经济和农村居民点布局特点,从自然地理条件、区位条件、生产条件以及生态环境条件4个方面建立评价指标体系,基于灰靶模型研究农村居民点布局适宜性,并据此提出相应的分类调控对策。研究结果表明:宜兴市农村居民点适宜性等级总体较高;在空间上呈现中、北部平原地区的农村居民点布局适宜性整体高于南部丘陵山区的分布特征。针对不同适宜等级提出的分类调控对策可以为当地农村土地整治、村庄规划等实际工作提供一定的参考。     

水资源可持续性的参数敏感性分析

通过界定敏感性参数的含义,认为参数的敏感性是系统对某一指参数的变化的敏感程度,以此为依据建立M-YAS模型分析参数的敏感性。以佛山市为例选取37 项影响水资源可持续的参数,分析水资源可持续性的参数敏感性问题,结果表明：37项反映水资源可持续性的参数中,仅13 项参数（生活用水定额、林牧渔畜用水量、工业用水定额、农田灌溉用水量、工业用水量、耕地灌溉定额、万元工业增加值用水量、火电冷却用水量、城乡生活用水量、工业节水、万元生产总值用水量、生活节水和农业节水）对于水资源可持续性的目标层而言是敏感性参数,且13项参数均来自用水结构与效率的准则层,说明区域水资源可持续性的目标层对用水结构与效率的准则层较为敏感。     

基于PCA算法的星载GNSS-R海面目标反演

提出使用主成分分析(principal components analysis,PCA)抑制时延多普勒图(delay Doppler map, DDM)中的海杂波,提高海面目标反演精度。以挪威Snøhvit采气平台作为海面目标,采用2016-11-13的DDM数据进行目标反演。结果显示,未使用PCA抑制海杂波前,反演位置平均误差为17.65 km;抑制海杂波后,反演位置平均误差为11.42 km,位置精度提升35.30%。     

高质量矿床的勘查战略

邮我国矿产勘查应以高质量矿床作为目标矿床的战略;认为勘查高质量矿床需要加强矿床模式的研究和ＧＩＳ技术的推广应用;确保高质量矿床战略成功实施的思路包括：（１）以高质量金矿床作为勘查目标;（２）加强对典型高质量矿床的研究,寻找同类型高质量矿床;（３）从构造环境发展演化的机理进行成矿分析,制定高质量矿床勘查战略;（４）打破地域限制,促进勘查队伍之间的竞争机制。     

胶东乳山金矿区原生晕地球化学特征及深部成矿预测

英格庄金矿以相对富As、Co、Ni而三甲和铜锡山金矿则以相对富Cu、Pb、Zn、Ag为特征.聚类分析显示,英格庄金矿具Bi+Hg、As+Sb+Co与Pb+Zn组合,三甲金矿具Bi+Hg+As+Sb+Co、Pb+Zn与Ag+Cu+Au组合,而铜锡山金矿则显示Bi+Hg+Sb+Pb+Ag、Cu+Au+Zn+As及Mo+Ni组合.结合Ni、Co、Mo与Ag、Hg、As、Sb等的相关特征,认为上述三个金矿均有成矿头尾晕叠加现象.Au、Ag、Cu、Pb、Zn、As、Sb、Bi、Hg、Mo等元素垂向变化规律显示,英格庄金矿在-200m标高以下、三甲金矿在-500m标高以下尚有成矿富集段存在.其中,所确定的英格庄金矿的靶位在16勘探线附近,三甲金矿的靶位则在16～20勘探线间.     

张量分类算法的遥感影像目标探测

提出了一种基于张量学习机的遥感影像目标探测方法。该方法基于张量数据模型和张量代数运算, 针对遥感影像数据多维或高维的特点, 将基于向量的监督法学习机扩展为基于张量的监督法学习机, 然后利用凸函数最优化理论和交互投影迭代法求得张量学习机的最优解。最后分别以高光谱遥感影像和高分辨率遥感影像为例, 使用张量学习机进行目标探测。实验表明, 与支持向量机等方法相比, 本文的方法在保持较高探测成功率的同时更好的抑制了虚警。     

极化SAR影像分类方法研究

采用不同的分类方法对荷兰Flevoland地区全极化数据进行分类,讨论了各分类方法存在的问题,通过比较得出基于复Wishart分布的最大似然监督分类的效果较好。     

多元素衬值累加地球化学方法在找矿中的应用——以青海省夏日哈-什多龙远景区为例

青海省夏日哈-什多龙成矿远景区位于青藏高原东昆仑东段,西起都兰县夏日哈镇,东至兴海县什多龙一带,跨祁漫塔格北坡-夏日哈岩浆弧和北昆仑岩浆弧,是青海省重要的成矿区。为了在该区寻找新的成矿有利地段,本文在对该区区域地质背景、矿产特征及区内典型矿床地质特征系统研究的基础上,利用多元素衬值累加地球化学找矿方法,在区域上优选出一批找矿靶区。多元素衬值累加地球化学找矿方法首先收集远景区1∶5万水系沉积物测量成果,经过数据处理,绘制远景区衬值累计地球化学图,进而建立典型矿床找矿模式,最终通过对比分析,选出新的找矿靶区。对圈定的找矿靶区进行野外踏勘检查,均发现不同程度的矿化线索,为今后在该区进一步找矿起到了很好的指导作用。通过本次工作,发现了该方法在靶区圈定方面的优势:(1)可快速圈定异常,提高工作效率;(2)编制的地球化学图浓集中心更显著;(3)圈定的找矿靶区找矿成果显著,尤其针对陆相火山岩型多金属矿、斑岩型铜钼矿、矽卡岩型多金属矿找矿成果突出;(4)能直观反映背景变化趋势,有助于评价异常。