# -*- coding: utf-8 -*-
"""ICM 2023 F 配图：24 张 SVG，复用 _svg 基元，输出到 papers/。
视角划分：Paper1 口径构建与全球减缓模型 / Paper2 巴西深析与抵抗分布 / Paper3 稳健性与政策建议。
"""
import os
import math
import xml.dom.minidom
import gen_icm2023f as G
HERE = os.path.dirname(os.path.abspath(__file__))
OUT = os.path.normpath(os.path.join(HERE, "..", "assets", "problems", "papers"))
os.makedirs(OUT, exist_ok=True)
from _svg import (_fig, _save, _bar, _grouped_bar, _line, _scatter, _pie,
_heatmap, _network, _flow, _txt, C_RED, C_GREEN, C_ACC, C_CYAN,
C_AMB, C_PUR, C_TEAL, C_PINK, C_MUT, C_TXT, C_GRID, PALETTE)
C_BLUE = C_ACC
C_AMBER = C_AMB
C_PURPLE = C_PUR
D = G.gen_icm2023f()
rows = D["rows"]
scen = D["scenarios"]
by_region = D["by_region"]
regions = D["regions"]
corr = D["corr"]
sens = D["sens"]
br = D["brazil"]
worth = D["worth_it"]
PHIS = D["PHIS"]
REGION_CN = D["REGION_CN"]
SHORT = {r["id"]: r["name"].split()[-1].replace(".", "") for r in rows}
NAMES = {r["id"]: r["name"] for r in rows}
REGION_COLOR = {"美洲": C_GREEN, "亚洲": C_RED, "欧洲": C_BLUE, "非洲": C_AMBER,
"大洋洲": C_TEAL, "中东": C_PURPLE}
def _by_id(i):
return next(r for r in rows if r["id"] == i)
def _save_check(name, svg):
xml.dom.minidom.parseString(svg)
_save(os.path.join(OUT, name), svg)
return name
# ---------- 自定义：侵蚀/分解柱（GDP 被 D_res/D_env 侵蚀 -> GGDP） ----------
def _erosion(title, items, W=760, H=440, note=""):
"""items: [(code, gdp, d_res, d_env, ggd)]"""
n = len(items)
bw = (W - 140) / (n + 1)
body = ""
for i, (code, gdp, dr, de, ggd) in enumerate(items):
x = 60 + (i + 1) * bw
mx = max(gdp for _, gdp, _, _, _ in items) * 1.05
if mx <= 0:
mx = 1
y0 = H - 100
full_h = (gdp / mx) * (H - 175)
ggd_h = (ggd / mx) * (H - 175)
d_h = full_h - ggd_h
# 全量轮廓
body += '\n' % (x, y0 - full_h, bw, full_h, C_BLUE)
# GGDP 实填（底部）
body += '\n' % (x, y0 - ggd_h, bw, ggd_h, C_BLUE)
# 被侵蚀段（顶部红）
body += '\n' % (x, y0 - full_h, bw, d_h, C_RED)
body += _txt(x + bw / 2, y0 - full_h - 6, "%.2f" % gdp, 10, C_MUT)
body += _txt(x + bw / 2, y0 - ggd_h - 6, "%.2f" % ggd, 11, C_BLUE, "middle", "700")
body += _txt(x + bw / 2, y0 + 16, code, 10, C_TXT)
body += _txt(120, 57, "■ GGDP(实填) ■ 自然资本损耗 D_res+D_env(红)", 11, C_TXT, "start")
if note:
body += _txt(W / 2, H - 14, note, 11, C_MUT)
return _fig(title, body, W, H)
# ---------- 自定义：敏感性 tornado（水平） ----------
def _tornado(title, sens_list, W=760, H=440):
body = ""
n = len(sens_list)
x0, x1 = 110, W - 60
vals = [s["base"] for s in sens_list] + [s["low"] for s in sens_list] + [s["high"] for s in sens_list]
vmin, vmax = min(vals), max(vals)
lo, hi = vmin * 0.95, vmax * 1.05
if hi == lo:
hi = lo + 1
def SX(v):
return x0 + (v - lo) / (hi - lo) * (x1 - x0)
y0 = 90
rh = (H - 150) / n
base_x = SX(sens_list[0]["base"])
for g in range(5):
gy = y0 + g / 4 * (H - 150 - y0)
body += '\n' % (x0, gy, x1, gy, C_GRID)
body += _txt(x0 - 6, gy + 4, "%.3f" % (hi - g / 4 * (hi - lo)), 10, C_MUT, "end")
for i, s in enumerate(sens_list):
cy = y0 + i * rh + rh / 2
bx = SX(s["base"])
lx = SX(s["low"]); hx = SX(s["high"])
body += '\n' % (min(lx, hx), cy - rh * 0.32, abs(hx - lx), rh * 0.64, C_AMBER)
body += '\n' % (bx, cy - rh * 0.4, bx, cy + rh * 0.4, C_RED)
body += _txt(x0 - 8, cy + 4, s["param"], 11, C_TXT, "end")
body += _txt(x1 + 4, cy + 4, "[%.3f, %.3f]" % (s["low"], s["high"]), 10, C_MUT, "start")
body += _txt((x0 + x1) / 2, H - 14, "红竖线=基准ΔT；琥珀条=参数±20%区间（°C）", 11, C_MUT)
return _fig(title, body, W, H)
# ============ Paper 1：口径构建与全球减缓模型 ============
def f1_1():
picks = ["美国 USA", "中国 China", "巴西 Brazil", "沙特 Saudi", "瑞士 Switzerland", "澳大利亚 Australia"]
items = []
for nm in picks:
r = next(x for x in rows if x["name"] == nm)
items.append((r["name"].split()[-1].replace(".", ""), r["gdp"], r["d_res"], r["d_env"], r["ggd"]))
return _erosion("图1 代表国 GGDP 分解：GDP 被自然资本损耗侵蚀为 GGDP", items,
note="红段=D_res+D_env（资源耗减+环境退化）；蓝段=GGDP（绿色比 1−res−env）")
def f1_2():
s = sorted(rows, key=lambda r: r["rank_drop"], reverse=True)
labels = [SHORT[r["id"]] for r in s]
vals = [r["rank_drop"] for r in s]
W, H = 760, 440
n = len(vals)
bw = (W - 140) / (n + 1)
body = ""
y0 = H - 95
mx = max(max(vals), abs(min(vals)), 0.1)
scale = (H - 175) / (2 * mx)
zero = y0 - (H - 175) / 2
body += '\n' % (60, zero, W - 60, zero, "#9ca3af")
for i, v in enumerate(vals):
x = 60 + (i + 1) * bw
h = v * scale
if v >= 0:
body += '\n' % (x, zero - h, bw, h, C_RED)
body += _txt(x + bw / 2, zero - h - 4, "%.1f" % v, 8, C_MUT)
else:
body += '\n' % (x, zero, bw, -h, C_GREEN)
body += _txt(x + bw / 2, zero + (-h) + 10, "%.1f" % v, 8, C_MUT)
body += _txt(x + bw / 2, y0 + 12, labels[i], 8, C_TXT)
body += _txt(120, 57, "■ 排名下降(红) ■ 排名上升(绿)", 11, C_TXT, "start")
body += _txt(W / 2, H - 14, "零线上方=相对地位受损，下方=受益（清洁国小幅上升）", 11, C_MUT)
return _fig("图2 各国排名变化（GGDP 相对 GDP 的位次变动）", body, W, H)
def f1_3():
s = sorted(rows, key=lambda r: r["ci"], reverse=True)[:12]
labels = [SHORT[r["id"]] for r in s]
vals = [r["ci"] for r in s]
colors = [REGION_COLOR[r["region"]] for r in s]
return _bar("图3 碳强度 TOP12（CO2/GDP，Gt/万亿$，按区域着色）", labels, vals, colors, fmt="%.3f")
def f1_4():
phis = PHIS
dC = [scen[p]["dC_global"] for p in phis]
dT = [scen[p]["dT"] for p in phis]
# 双轴：柱=ΔC(左)，折线=ΔT(右)
W, H = 760, 440
x0, x1 = 90, W - 60
y0, y1 = H - 90, 70
mxC = max(dC) * 1.15
mxT = max(dT) * 1.4
bw = 90
body = ""
for g in range(5):
gy = y1 + g / 4 * (y0 - y1)
body += '\n' % (x0, gy, x1, gy, C_GRID)
body += _txt(x0 - 8, gy + 4, "%.0f" % (mxC - g / 4 * mxC), 10, C_MUT, "end")
for i, p in enumerate(phis):
cx = x0 + 120 + i * 180
hC = (dC[i] / mxC) * (y0 - y1)
body += '\n' % (cx - bw / 2, y0 - hC, bw, hC, C_BLUE)
body += _txt(cx, y0 - hC - 6, "%.0f" % dC[i], 11, C_BLUE, "middle", "700")
# ΔT 折线（右轴）
poly = []
for i, p in enumerate(phis):
cx = x0 + 120 + i * 180
sy = y1 + (1 - dT[i] / mxT) * (y0 - y1)
poly.append((cx, sy))
body += '\n' % (" ".join("%g,%g" % (a, b) for a, b in poly), C_RED)
for i, (a, b) in enumerate(poly):
body += '\n' % (a, b, C_RED)
body += _txt(a, b - 12, "%.3f°C" % dT[i], 10, C_RED, "middle", "700")
for i, p in enumerate(phis):
cx = x0 + 120 + i * 180
body += _txt(cx, y0 + 18, "φ=%.1f" % p, 11, C_TXT)
body += _txt(120, 57, "■ ΔC 累计避免CO2(Gt,左轴)", 11, C_BLUE, "start")
body += _txt(320, 57, "● ΔT 增温放缓(°C,右轴)", 11, C_RED, "start")
body += _txt(W / 2, H - 14, "全球采纳比例越高，气候减缓影响越大（线性可辩护）", 11, C_MUT)
return _fig("图4 全球减缓影响随采纳比例 φ 的变化（ΔC 与 ΔT）", body, W, H)
def f1_5():
phis = PHIS
benefit = [scen[p]["benefit"] / 1e12 for p in phis]
cost = [scen[p]["cost"] / 1e12 for p in phis]
net = [scen[p]["net"] / 1e12 for p in phis]
groups = ["φ=0.3", "φ=0.6", "φ=1.0"]
cats = ["气候效益", "转轨成本", "净收益"]
vals = [[benefit[0], cost[0], net[0]], [benefit[1], cost[1], net[1]], [benefit[2], cost[2], net[2]]]
svg = _grouped_bar("图5 气候效益 vs 转轨成本 vs 净收益（单位：万亿美元）", groups, cats, vals, fmt="%.2f")
return svg
def f1_6():
return _tornado("图6 敏感性 tornado：ΔT(φ=1) 对各参数 ±20% 的响应（°C）", sens)
def f1_7():
return _flow("图7 方法论流程：从口径选择到一页报告", [
("选口径", "SEEA式\nGGDP"),
("建模型", "减排响应\nr=R·φ·s"),
("估全球影响", "ΔC/ΔT/$"),
("值得性", "BCR=39"),
("国别深析", "巴西"),
("一页报告", "致领导人"),
])
def f1_8():
groups = [REGION_CN[r] for r in regions]
cats = ["均值绿色比", "均值抵抗"]
vals = [[by_region[r]["mean_green"], by_region[r]["mean_resist"]] for r in regions]
return _grouped_bar("图8 各区域绿色比（清洁度）与抵抗指数对比", groups, cats, vals, fmt="%.3f")
# ============ Paper 2：巴西深析与抵抗分布 ============
def f2_1():
items = [(SHORT[br["id"]], br["gdp"], br["d_res"], br["d_env"], br["ggd"])]
return _erosion("图1 巴西 GGDP 分解：2.00 万亿$ GDP 中 0.42 万亿$ 为自然资本损耗", items,
note="绿色比=0.790；D_env=0.240(含森林退减) 高于 D_res=0.180")
def f2_2():
peers = ["加拿大 Canada", "俄罗斯 Russia", "韩国 S.Korea", "澳大利亚 Australia", "墨西哥 Mexico", "西班牙 Spain", "巴西 Brazil", "意大利 Italy"]
items = []
for nm in peers:
r = next(x for x in rows if x["name"] == nm)
items.append((r["name"].split()[-1].replace(".", ""), r["gdp"], r["d_res"], r["d_env"], r["ggd"]))
return _erosion("图2 巴西与同量级经济体绿色比对比（红段越小越清洁）", items,
note="巴西红段占比 21%，高于多数同量级国家 → 自然资本依赖高")
def f2_3():
yrs = list(range(0, 28))
bau, ggd = [], []
for t in yrs:
bau.append((2023 + t, 0.48 * (1.015 ** t)))
ggd.append((2023 + t, 0.48 - (0.48 - br["baa_emis"]) * (min(t, 12) / 12.0)))
return _line("图3 巴西排放路径：BAU 缓增 vs GGDP 稳态下降", [
("BAU(维持GDP导向)", C_RED, bau),
("GGDP导向(稳态0.40 Gt)", C_GREEN, ggd),
], xlabel="年份", ylabel="年排放 Gt/yr")
def f2_4():
s = sorted(rows, key=lambda r: r["resist"], reverse=True)[:12]
labels = [SHORT[r["id"]] for r in s]
vals = [r["resist"] for r in s]
colors = [REGION_COLOR[r["region"]] for r in s]
return _bar("图4 抵抗指数 TOP12（res+env，越高越倾向抵制转轨）", labels, vals, colors, fmt="%.3f")
def f2_5():
series = []
for rg in regions:
pts = [(r["dev"], r["green"]) for r in rows if r["region"] == rg]
series.append((REGION_CN[rg], REGION_COLOR[rg], pts))
return _scatter("图5 发展指数 vs 绿色比（按区域着色，清洁国居上）", series,
xlabel="发展指数 dev", ylabel="绿色比 green_ratio")
def f2_6():
labels = [REGION_CN[r] for r in regions]
vals = [by_region[r]["avoided_full"] for r in regions]
colors = [REGION_COLOR[r] for r in regions]
return _bar("图6 各区域满采纳累计避免 CO2（Gt，亚洲贡献最大）", labels, vals, colors, fmt="%.1f")
def f2_7():
series = [("抵抗 vs 排名下降", C_PURPLE, [(r["resist"], r["rank_drop"]) for r in rows])]
return _scatter("图7 抵抗指数 vs 排名下降（相关 0.57，资源国受损更明显）", series,
xlabel="抵抗指数 resist", ylabel="排名下降 rank_drop")
def f2_8():
return _flow("图8 国别深析流程：巴西", [
("选巴西", "森林/资源\n依赖高"),
("算GGDP", "1.58万亿$"),
("排放路径", "BAU vs GGDP"),
("成本效益", "净$1.0e11"),
("一页报告", "建议采用"),
])
# ============ Paper 3：稳健性与政策建议 ============
def f3_1():
phis = PHIS
dT = [scen[p]["dT"] for p in phis]
base = dT[2]
lo = [base * 0.8 * (p / 1.0) for p in phis]
hi = [base * 1.2 * (p / 1.0) for p in phis]
W, H = 760, 440
x0, x1 = 90, W - 60
y0, y1 = H - 90, 70
mx = max(hi) * 1.15
if mx <= 0:
mx = 1
body = ""
for g in range(5):
gy = y1 + g / 4 * (y0 - y1)
body += '\n' % (x0, gy, x1, gy, C_GRID)
body += _txt(x0 - 8, gy + 4, "%.3f" % (mx - g / 4 * mx), 10, C_MUT, "end")
xs = [x0 + 120 + i * 180 for i in range(len(phis))]
# 不确定带
poly = " ".join("%g,%g" % (xs[i], y1 + (1 - hi[i] / mx) * (y0 - y1)) for i in range(len(phis)))
poly += " " + " ".join("%g,%g" % (xs[i], y1 + (1 - lo[i] / mx) * (y0 - y1)) for i in range(len(phis) - 1, -1, -1))
body += '\n' % (poly, C_AMBER)
body += '\n' % (" ".join("%g,%g" % (xs[i], y1 + (1 - dT[i] / mx) * (y0 - y1)) for i in range(len(phis))), C_RED)
for i in range(len(phis)):
body += '\n' % (xs[i], y1 + (1 - dT[i] / mx) * (y0 - y1), C_RED)
body += _txt(xs[i], y0 + 18, "φ=%.1f" % phis[i], 11, C_TXT)
body += _txt(xs[i], y1 + (1 - dT[i] / mx) * (y0 - y1) - 10, "%.3f°C" % dT[i], 10, C_RED, "middle", "700")
body += _txt(120, 57, "红=基准ΔT；琥珀带=参数±20%不确定区间", 11, C_TXT, "start")
body += _txt(W / 2, H - 14, "即使参数全面保守(−20%)，ΔT 仍达 0.07°C（正向且显著）", 11, C_MUT)
return _fig("图1 增温放缓 ΔT 的不确定性带（参数 ±20%）", body, W, H)
def f3_2():
series = [("碳强度 vs 敏感系数", C_TEAL, [(r["ci"], r["sens"]) for r in rows])]
return _scatter("图2 碳强度 vs 减排敏感系数（相关 0.67，机制成立）", series,
xlabel="碳强度 CO2/GDP", ylabel="敏感系数 s")
def f3_3():
picks = ["瑞士 Switzerland", "巴西 Brazil", "沙特 Saudi"]
labels = [next(x for x in rows if x["name"] == nm)["name"].split()[-1].replace(".", "") for nm in picks]
res = [next(x for x in rows if x["name"] == nm)["res"] for nm in picks]
env = [next(x for x in rows if x["name"] == nm)["env"] for nm in picks]
vals = [[res[i], env[i]] for i in range(len(picks))]
return _grouped_bar("图3 自然资本依赖度对比（res / env 占比，巴西 21%% 居前）", labels, ["资源耗减res", "环境退化env"], vals, fmt="%.3f")
def f3_4():
labels = ["巴西气候效益", "巴西转轨成本", "巴西净收益"]
vals = [br["benefit"] / 1e8, br["cost"] / 1e8, br["net"] / 1e8]
colors = [C_GREEN, C_RED, C_BLUE]
return _bar("图4 巴西成本效益（单位：亿$）", labels, vals, colors, fmt="%.1f")
def f3_5():
# 一页非技术报告 flyer（560×760）
W, H = 560, 760
body = ""
body += '\n' % (W, H)
body += '\n' % (W, C_GREEN)
body += _txt(W / 2, 30, "一页非技术报告 · 致巴西领导人", 18, "#ffffff", "middle", "700")
body += _txt(W / 2, 52, "关于采用绿色 GDP（GGDP）作为国家经济健康主要指标的建议", 11, "#e8f5e9", "middle")
# 现状
y = 100
body += _txt(28, y, "一、现状：今天的 GDP 隐瞒了自然资本的代价", 14, C_TXT, "start", "700"); y += 26
body += _txt(28, y, "巴西 GDP 约 2.00 万亿$，但其中 0.42 万亿$（21%）来自森林与", 12, C_MUT, "start"); y += 20
body += _txt(28, y, "资源耗减——这部分在 GDP 里被记为“增长”，实则透支未来。", 12, C_MUT, "start"); y += 30
# 问题
body += _txt(28, y, "二、问题：继续以 GDP 为纲的代价", 14, C_TXT, "start", "700"); y += 26
body += _txt(28, y, "· 森林退减推高短期 GDP，却削弱气候韧性与长期福祉；", 12, C_MUT, "start"); y += 20
body += _txt(28, y, "· 我们的碳强度（0.24 Gt/万亿$）高于多数同量级国家。", 12, C_MUT, "start"); y += 30
# 建议
body += _txt(28, y, "三、建议：采用 GGDP", 14, C_GREEN, "start", "700"); y += 26
body += _txt(28, y, "· 把森林与资源损耗计入核算，让“保护”成为“增长”；", 12, C_MUT, "start"); y += 20
body += _txt(28, y, "· 模型显示：满采纳可使巴西年排放降 16%、累计避免 2.1 Gt，", 12, C_MUT, "start"); y += 20
body += _txt(28, y, " 净收益约 1000 亿美元，远超转轨成本（约 37 亿美元）。", 12, C_MUT, "start"); y += 30
# 行动
body += _txt(28, y, "四、三步走行动", 14, C_TXT, "start", "700"); y += 26
body += _txt(28, y, "1) 建立森林与碳账户的官方 GGDP 卫星账户；", 12, C_MUT, "start"); y += 20
body += _txt(28, y, "2) 将 GGDP 用于保护区与农业补贴的绩效评估；", 12, C_MUT, "start"); y += 20
body += _txt(28, y, "3) 在国际谈判中以 GGDP 争取气候资金与公允排名。", 12, C_MUT, "start"); y += 34
# 结论框
body += '\n' % (y, W - 56, C_GREEN)
body += _txt(40, y + 24, "结论：建议巴西支持并率先采用 GGDP，", 13, C_GREEN, "start", "700")
body += _txt(40, y + 44, "把自然资本变成国家竞争力的新引擎。", 13, C_GREEN, "start", "700")
body += _txt(W - 28, H - 20, "数据：本站合成演示模型（SEED 2023）", 9, C_MUT, "end")
return _fig("图5 一页非技术报告（致巴西领导人）", body, W, H)
def f3_6():
labels = [REGION_CN[r] for r in regions]
vals = [by_region[r]["avoided_full"] for r in regions]
colors = [REGION_COLOR[r] for r in regions]
total = sum(vals)
pct = [v / total * 100 for v in vals]
svg = _bar("图6 各区域减缓贡献占比（亚洲主导，占 %.0f%%）" % pct[labels.index("亚洲")],
labels, vals, colors, fmt="%.1f")
return svg
def f3_7():
yrs = list(range(0, 28))
series = []
for p in PHIS:
dCg = scen[p]["dC_global"]
series.append(("φ=%.1f" % p, C_BLUE if p == 0.3 else (C_AMBER if p == 0.6 else C_RED),
[(2023 + t, dCg * (t / 27.0)) for t in yrs]))
return _line("图7 累计避免 CO2 随时间的情景累积（至 2050）", series, xlabel="年份", ylabel="累计避免 Gt CO2")
def f3_8():
return _flow("图8 治理闭环：采用—激励—核算—补偿—迭代", [
("采用GGDP", "替换头条\n指标"),
("激励减排", "压降 D_env"),
("监测核算", "卫星账户"),
("抵抗补偿", "公正转轨\n基金"),
("迭代优化", "灵敏度\n复盘"),
])
# ---------------------------------------------------------------------------
JOBS = [f1_1, f1_2, f1_3, f1_4, f1_5, f1_6, f1_7, f1_8,
f2_1, f2_2, f2_3, f2_4, f2_5, f2_6, f2_7, f2_8,
f3_1, f3_2, f3_3, f3_4, f3_5, f3_6, f3_7, f3_8]
def main():
bad = 0
for i, job in enumerate(JOBS, 1):
try:
svg = job()
name = "icm2023f-%d-fig%d.svg" % ((i - 1) // 8 + 1, (i - 1) % 8 + 1)
_save_check(name, svg)
except Exception as e:
bad += 1
print("FAIL", i, job.__name__, e)
print("icm2023f figures: %d generated, %d bad" % (len(JOBS), bad))
if __name__ == "__main__":
main()